diff --git a/README.md b/README.md index 8bd3334..4321835 100644 --- a/README.md +++ b/README.md @@ -583,7 +583,79 @@ difference; the matrices come from the same manifest, split, seed, and class ord ![Paired confusion matrices](artifacts/figures/final/figH_confusion_matrix_5gnidd_a0.75_seed0.png) -## 17. Main Scientific Findings +## 17. Deep Comparison: Original PROTEAN vs Proposed (Result) + +A dedicated paired analysis compares `original_protean` against +`support_weighted` at the validation-selected beta for every dataset-alpha condition, using the +tests split and hard-coded pairing checks (same dataset, alpha, seed, and `manifest_content_hash`). +Full report: **`docs/research/ORIGINAL_VS_PROPOSED_DEEP_COMPARISON.md`**. Derived tables: +`artifacts/analysis/deepcmp__*.csv` (produced by `scripts/deep_comparison.py`). Figures: +`scripts/make_comparison_figures.py`. + +**Global test overview** (Δ = proposed − original, mean over seeds `{0,1,2}`; rare-class macro-F1 shown +in full, other metrics as deltas): + +| Dataset | alpha | beta* | rare-F1 original | rare-F1 proposed | Δ rare-F1 | Δ rare-recall | Δ macro-F1 | Δ accuracy | +|---|---|---|---|---|---|---|---|---| +| X-IIoTID | 0.25 | 0.00 | 0.6328 | 0.6321 | −0.0007 | −0.0007 | −0.0007 | −0.0014 | +| X-IIoTID | 0.50 | 0.75 | 0.6425 | 0.6655 | **+0.0230** | −0.0144 | +0.0222 | +0.0069 | +| X-IIoTID | 0.75 | 0.50 | 0.6039 | 0.6052 | +0.0014 | −0.0077 | +0.0058 | +0.0075 | +| 5G-NIDD | 0.25 | 0.00 | 0.8423 | 0.8412 | −0.0010 | +0.0007 | −0.0028 | −0.0007 | +| 5G-NIDD | 0.50 | 1.00 | 0.8805 | 0.8720 | −0.0086 | −0.0058 | −0.0038 | −0.0007 | +| 5G-NIDD | 0.75 | 0.50 | 0.8799 | 0.8967 | **+0.0168** | −0.0015 | +0.0117 | +0.0013 | + +Both `alpha=0.25` conditions selected `beta=0` and are numerically null. Among the four genuine +`beta>0` conditions, rare-class macro-F1 improved in three and degraded in one; overall macro-F1 moves +with it, while rare-class recall usually falls (precision–recall trade-off). 5G-NIDD a0.50 is +seed-dominated. No statistical significance is claimed. + +### Fig I — Paired per-client test rare-class macro-F1 (proposed vs ORIGINAL PROTEAN) + +Each point is one (client, seed) test observation, faceted by dataset and alpha; charcoal/teal/red/gray +and markers encode outcome and seed. At `beta>0` conditions the response is client-specific (e.g. +X-IIoTID a0.50: 12/30 improved, 1 degraded, 17 unchanged), with individual changes larger than the +condition mean; at `beta=0` conditions almost all observations are unchanged. + +![Paired per-client comparison](artifacts/figures/final/figI_per_client_original_vs_proposed.png) + +### Fig J — Per-class test F1 change (beta>0 conditions) + +Signed Δ F1 per class, panels per dataset-alpha; class support is annotated. Largest gains are +X-IIoTID `Tampering` (+0.14) and `Reconnaissance` (+0.05) and 5G-NIDD `TCPConnectScan` (+0.03) / +`ICMPFlood` (+0.10, tiny support); largest losses are X-IIoTID `C&C` (−0.06) and 5G-NIDD `SYNFlood` +(−0.04). Benign detection is essentially unchanged. + +![Per-class comparison](artifacts/figures/final/figJ_per_class_original_vs_proposed.png) + +### Fig K — Confusion-matrix difference (proposed − original) + +For the paired condition 5G-NIDD, `alpha=0.75`, `seed=0` (identical manifest, split, class order, +243,177 test samples): left = raw count difference, right = row-normalized rate difference. The +dominant error `Benign → UDPFlood` is essentially unchanged (56,899 → 57,054, +155). The main reduced +errors are `HTTPFlood → SlowrateDoS` (−547) and `Benign → TCPConnectScan` (−520); the main increased +error is `SlowrateDoS → HTTPFlood` (+229). Red = positive delta, blue = negative; no significance is +inferred. + +![Confusion-matrix difference](artifacts/figures/final/figK_confusion_delta_5gnidd_a0.75_seed0.png) + +**Per-client findings.** The proposed method redistributes benefit: within a condition some clients +improve and others degrade, and the low-rare-support quartile is not systematically disadvantaged +(descriptive; Pearson Δ-vs-support-fraction ≈ −0.09, n=120). Client ids are per-seed realizations. + +**Per-class findings.** Rare-class gains are partly precision-driven (recall often falls) and are not +confined to rare classes; the largest regressions are on low-support attack classes (X-IIoTID `C&C`, +5G-NIDD `SYNFlood`). + +**Confusion-delta findings.** The weighting does not fix the dominant Benign→UDPFlood error; the +measurable benefit at the analysed pair is a reduced HTTPFlood↔SlowrateDoS confusion, with a small +SlowrateDoS recall cost. + +**Practical implications and limitations.** Effects are small (≤ ~2.3 pp rare-F1) and +condition-dependent; the largest gain is comparable to seed noise. Practical benefit is modest, not a +blanket improvement, and comes with rare-recall and some client- or class-level costs. Pairing, +metric definitions, and threats to validity are detailed in the report. + +## 18. Main Scientific Findings 1. **Changing `beta` changes the global prototype.** Contribution weights become proportional to support, and for a fixed snapshot mean prototype displacement grows monotonically with `beta` in @@ -602,7 +674,7 @@ difference; the matrices come from the same manifest, split, seed, and class ord not a proxy for detection quality. 8. **The dominant classification error (Benign → UDPFlood) is not addressed by `beta`.** -## 18. Limitations and Novelty Boundary +## 19. Limitations and Novelty Boundary **Limitations.** @@ -623,7 +695,7 @@ contribution is the controlled `beta` parameterization inside PROTEAN, the valid condition-wise selection, the paired multi-seed design, and the behavioral findings. No claim is made that the method universally improves PROTEAN. -## 19. Repository Structure +## 20. Repository Structure ``` src/protean_research/ data loading/preprocessing/partitioning, models, aggregation, federated @@ -643,7 +715,7 @@ artifacts/figures/final/ thesis-ready figures results/ local run outputs (git-ignored; 108 non-smoke run dirs + 1 smoke) ``` -## 20. Reproduction and Tests +## 21. Reproduction and Tests ```bash # unit + integration tests (expected: 75 passed) @@ -682,7 +754,7 @@ rerun the grid. the shared `S_j`. - `beta` selection uses validation only; the test set is never used for selection. -## 21. Complete Artifact Index +## 22. Complete Artifact Index Per-run outputs (`results/__alpha__seed__/`): @@ -720,7 +792,7 @@ Analysis outputs (`artifacts/analysis/`), by function: | Prototype snapshots & weights | `*__prototype_snapshot.json`, `*__prototype_weights.csv`, `*__prototype_effect*.csv` | Partition manifests: `artifacts/partitions/__alpha__seed.json` (18 files). -Figures: `artifacts/figures/final/` — 9 figures (figA–figH) as PNG (220 dpi) plus matching vector PDF; +Figures: `artifacts/figures/final/` — 12 figures (figA–figK) as PNG (220 dpi) plus matching vector PDF; regenerated reproducibly by `scripts/make_final_figures.py`. Key documents: @@ -729,6 +801,7 @@ Key documents: |---|---| | `docs/research/EXPERIMENT_PROTOCOL.md` | frozen reproduction protocol | | `docs/research/FINAL_EXPERIMENTAL_SYNTHESIS.md` | authoritative six-condition synthesis | +| `docs/research/ORIGINAL_VS_PROPOSED_DEEP_COMPARISON.md` | paired original-vs-proposed deep comparison | | `docs/research/CROSS_DATASET_BETA_ANALYSIS.md` | cross-dataset comparison | | `docs/research/XIIOTID_CROSS_ALPHA_ANALYSIS.md` | X-IIoTID cross-alpha analysis | | `docs/research/5GNIDD_CROSS_ALPHA_ANALYSIS.md` | 5G-NIDD cross-alpha analysis | @@ -741,7 +814,7 @@ Key documents: | `docs/handoff/SESSION_HANDOFF.md` | current state and next steps | | `planningawal/PROTEAN_Revisi_Lurus.md` | immutable research specification | -## 22. References +## 23. References - Chennoufi, S., Han, Y., Blanc, G., De Cristofaro, E., & Kiennert, C. (2025). PROTEAN: Federated Intrusion Detection in Non-IID Environments through Prototype-Based Knowledge Sharing. ESORICS diff --git a/artifacts/analysis/deepcmp__client_support_join.csv b/artifacts/analysis/deepcmp__client_support_join.csv new file mode 100644 index 0000000..3163e9f --- /dev/null +++ b/artifacts/analysis/deepcmp__client_support_join.csv @@ -0,0 +1,121 @@ +dataset,alpha,seed,client,delta,rare_support,total,rare_frac,present_classes,missing +xiiotid,0.5,0,0,-0.0005042259896347323,144,94564,0.001522778224271393,7,2 +xiiotid,0.5,0,1,0.008524445085661969,249,50631,0.004917935652070866,9,0 +xiiotid,0.5,0,2,0.018813433543958924,97,52476,0.0018484640597606526,9,0 +xiiotid,0.5,0,3,-0.2565947651121153,770,69609,0.011061787987185566,9,0 +xiiotid,0.5,0,4,0.014800433413590774,19,18521,0.0010258625344203877,9,0 +xiiotid,0.5,0,5,0.13628930367827585,6,41886,0.00014324595330181923,9,0 +xiiotid,0.5,0,6,0.08483385422643441,137,45085,0.003038704668958634,7,2 +xiiotid,0.5,0,7,-0.005389002939121236,30,84396,0.00035546708374804495,9,0 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+xiiotid,0.25,1,Reconnaissance,Normal,2081,2071,-10,0.08169113606029678,0.08129857894323624,-0.0003925571170605435 +xiiotid,0.25,1,Reconnaissance,RDOS,0,0,0,0.0,0.0,0.0 +xiiotid,0.25,1,Reconnaissance,Reconnaissance,16137,16132,-5,0.633469419800581,0.6332731412420507,-0.00019627855853032727 +xiiotid,0.25,1,Reconnaissance,Tampering,0,0,0,0.0,0.0,0.0 +xiiotid,0.25,1,Reconnaissance,Weaponization,1358,1350,-8,0.053309256496820286,0.05299521080317186,-0.00031404569364842233 +xiiotid,0.25,1,Tampering,C&C,0,0,0,0.0,0.0,0.0 +xiiotid,0.25,1,Tampering,Exfiltration,0,0,0,0.0,0.0,0.0 +xiiotid,0.25,1,Tampering,Exploitation,4,4,0,0.00390625,0.00390625,0.0 +xiiotid,0.25,1,Tampering,Lateral_movement,11,11,0,0.0107421875,0.0107421875,0.0 +xiiotid,0.25,1,Tampering,Normal,0,0,0,0.0,0.0,0.0 +xiiotid,0.25,1,Tampering,RDOS,0,0,0,0.0,0.0,0.0 +xiiotid,0.25,1,Tampering,Reconnaissance,0,0,0,0.0,0.0,0.0 +xiiotid,0.25,1,Tampering,Tampering,1009,1009,0,0.9853515625,0.9853515625,0.0 +xiiotid,0.25,1,Tampering,Weaponization,0,0,0,0.0,0.0,0.0 +xiiotid,0.25,1,Weaponization,C&C,0,1,1,0.0,7.76156473144986e-05,7.76156473144986e-05 +xiiotid,0.25,1,Weaponization,Exfiltration,0,0,0,0.0,0.0,0.0 +xiiotid,0.25,1,Weaponization,Exploitation,2,2,0,0.0001552312946289972,0.0001552312946289972,0.0 +xiiotid,0.25,1,Weaponization,Lateral_movement,0,0,0,0.0,0.0,0.0 +xiiotid,0.25,1,Weaponization,Normal,1,0,-1,7.76156473144986e-05,0.0,-7.76156473144986e-05 +xiiotid,0.25,1,Weaponization,RDOS,0,0,0,0.0,0.0,0.0 +xiiotid,0.25,1,Weaponization,Reconnaissance,0,0,0,0.0,0.0,0.0 +xiiotid,0.25,1,Weaponization,Tampering,0,0,0,0.0,0.0,0.0 +xiiotid,0.25,1,Weaponization,Weaponization,12881,12881,0,0.9997671530580565,0.9997671530580565,0.0 +xiiotid,0.25,2,C&C,C&C,522,522,0,0.9109947643979057,0.9109947643979057,0.0 +xiiotid,0.25,2,C&C,Exfiltration,0,0,0,0.0,0.0,0.0 +xiiotid,0.25,2,C&C,Exploitation,1,1,0,0.0017452006980802793,0.0017452006980802793,0.0 +xiiotid,0.25,2,C&C,Lateral_movement,0,0,0,0.0,0.0,0.0 +xiiotid,0.25,2,C&C,Normal,50,50,0,0.08726003490401396,0.08726003490401396,0.0 +xiiotid,0.25,2,C&C,RDOS,0,0,0,0.0,0.0,0.0 +xiiotid,0.25,2,C&C,Reconnaissance,0,0,0,0.0,0.0,0.0 +xiiotid,0.25,2,C&C,Tampering,0,0,0,0.0,0.0,0.0 +xiiotid,0.25,2,C&C,Weaponization,0,0,0,0.0,0.0,0.0 +xiiotid,0.25,2,Exfiltration,C&C,0,0,0,0.0,0.0,0.0 +xiiotid,0.25,2,Exfiltration,Exfiltration,3387,3311,-76,0.7650779308786989,0.74791054890445,-0.01716738197424894 +xiiotid,0.25,2,Exfiltration,Exploitation,0,0,0,0.0,0.0,0.0 +xiiotid,0.25,2,Exfiltration,Lateral_movement,0,0,0,0.0,0.0,0.0 +xiiotid,0.25,2,Exfiltration,Normal,0,0,0,0.0,0.0,0.0 +xiiotid,0.25,2,Exfiltration,RDOS,0,0,0,0.0,0.0,0.0 +xiiotid,0.25,2,Exfiltration,Reconnaissance,0,0,0,0.0,0.0,0.0 +xiiotid,0.25,2,Exfiltration,Tampering,0,0,0,0.0,0.0,0.0 +xiiotid,0.25,2,Exfiltration,Weaponization,1040,1116,76,0.23492206912130112,0.25208945109555003,0.017167381974248913 +xiiotid,0.25,2,Exploitation,C&C,0,0,0,0.0,0.0,0.0 +xiiotid,0.25,2,Exploitation,Exfiltration,0,0,0,0.0,0.0,0.0 +xiiotid,0.25,2,Exploitation,Exploitation,140,139,-1,0.6167400881057269,0.6123348017621145,-0.004405286343612369 +xiiotid,0.25,2,Exploitation,Lateral_movement,0,0,0,0.0,0.0,0.0 +xiiotid,0.25,2,Exploitation,Normal,22,22,0,0.09691629955947137,0.09691629955947137,0.0 +xiiotid,0.25,2,Exploitation,RDOS,0,0,0,0.0,0.0,0.0 +xiiotid,0.25,2,Exploitation,Reconnaissance,65,66,1,0.28634361233480177,0.2907488986784141,0.0044052863436123135 +xiiotid,0.25,2,Exploitation,Tampering,0,0,0,0.0,0.0,0.0 +xiiotid,0.25,2,Exploitation,Weaponization,0,0,0,0.0,0.0,0.0 +xiiotid,0.25,2,Lateral_movement,C&C,10,10,0,0.001582528881152081,0.001582528881152081,0.0 +xiiotid,0.25,2,Lateral_movement,Exfiltration,0,0,0,0.0,0.0,0.0 +xiiotid,0.25,2,Lateral_movement,Exploitation,1234,1206,-28,0.1952840639341668,0.19085298306694098,-0.004431080867225823 +xiiotid,0.25,2,Lateral_movement,Lateral_movement,5043,5073,30,0.7980693147649944,0.8028169014084507,0.004747586643456314 +xiiotid,0.25,2,Lateral_movement,Normal,4,4,0,0.0006330115524608325,0.0006330115524608325,0.0 +xiiotid,0.25,2,Lateral_movement,RDOS,0,0,0,0.0,0.0,0.0 +xiiotid,0.25,2,Lateral_movement,Reconnaissance,4,4,0,0.0006330115524608325,0.0006330115524608325,0.0 +xiiotid,0.25,2,Lateral_movement,Tampering,22,20,-2,0.003481563538534578,0.003165057762304162,-0.0003165057762304162 +xiiotid,0.25,2,Lateral_movement,Weaponization,2,2,0,0.0003165057762304162,0.0003165057762304162,0.0 +xiiotid,0.25,2,Normal,C&C,4849,5044,195,0.05796702968284898,0.06029814347706543,0.0023311137942164453 +xiiotid,0.25,2,Normal,Exfiltration,3,3,0,3.586328914179149e-05,3.586328914179149e-05,0.0 +xiiotid,0.25,2,Normal,Exploitation,778,774,-4,0.009300546317437926,0.009252728598582205,-4.781771885572182e-05 +xiiotid,0.25,2,Normal,Lateral_movement,226,237,11,0.0027017011153482925,0.002833199842201528,0.00013149872685323544 +xiiotid,0.25,2,Normal,Normal,77052,76831,-221,0.9211127183177726,0.918470789350994,-0.002641928966778617 +xiiotid,0.25,2,Normal,RDOS,229,225,-4,0.002737564404490084,0.002689746685634362,-4.781771885572182e-05 +xiiotid,0.25,2,Normal,Reconnaissance,69,69,0,0.0008248556502612042,0.0008248556502612042,0.0 +xiiotid,0.25,2,Normal,Tampering,134,123,-11,0.0016018935816666867,0.0014703948548134512,-0.00013149872685323544 +xiiotid,0.25,2,Normal,Weaponization,311,345,34,0.0037178276410323847,0.0041242782513060215,0.0004064506102736368 +xiiotid,0.25,2,RDOS,C&C,50,49,-1,0.001769786209825853,0.001734390485629336,-3.539572419651712e-05 +xiiotid,0.25,2,RDOS,Exfiltration,0,0,0,0.0,0.0,0.0 +xiiotid,0.25,2,RDOS,Exploitation,0,0,0,0.0,0.0,0.0 +xiiotid,0.25,2,RDOS,Lateral_movement,0,0,0,0.0,0.0,0.0 +xiiotid,0.25,2,RDOS,Normal,30,30,0,0.001061871725895512,0.001061871725895512,0.0 +xiiotid,0.25,2,RDOS,RDOS,28172,28172,0,0.9971683420642786,0.9971683420642786,0.0 +xiiotid,0.25,2,RDOS,Reconnaissance,0,0,0,0.0,0.0,0.0 +xiiotid,0.25,2,RDOS,Tampering,0,1,1,0.0,3.539572419651706e-05,3.539572419651706e-05 +xiiotid,0.25,2,RDOS,Weaponization,0,0,0,0.0,0.0,0.0 +xiiotid,0.25,2,Reconnaissance,C&C,158,160,2,0.00620240244955641,0.006280913872968517,7.851142341210645e-05 +xiiotid,0.25,2,Reconnaissance,Exfiltration,1,1,0,3.925571170605323e-05,3.925571170605323e-05,0.0 +xiiotid,0.25,2,Reconnaissance,Exploitation,6118,6025,-93,0.24016644421763367,0.2365156630289707,-0.003650781188662966 +xiiotid,0.25,2,Reconnaissance,Lateral_movement,13,14,1,0.000510324252178692,0.0005495799638847453,3.9255711706053225e-05 +xiiotid,0.25,2,Reconnaissance,Normal,2194,2192,-2,0.08612703148308079,0.08604852005966868,-7.851142341211426e-05 +xiiotid,0.25,2,Reconnaissance,RDOS,0,0,0,0.0,0.0,0.0 +xiiotid,0.25,2,Reconnaissance,Reconnaissance,16382,16498,116,0.6430870691685641,0.6476407317264662,0.004553662557902127 +xiiotid,0.25,2,Reconnaissance,Tampering,10,11,1,0.0003925571170605323,0.00043181282876658553,3.9255711706053225e-05 +xiiotid,0.25,2,Reconnaissance,Weaponization,598,573,-25,0.02347491560021983,0.0224935228075685,-0.000981392792651331 +xiiotid,0.25,2,Tampering,C&C,3,3,0,0.0029296875,0.0029296875,0.0 +xiiotid,0.25,2,Tampering,Exfiltration,0,0,0,0.0,0.0,0.0 +xiiotid,0.25,2,Tampering,Exploitation,0,0,0,0.0,0.0,0.0 +xiiotid,0.25,2,Tampering,Lateral_movement,232,234,2,0.2265625,0.228515625,0.001953125 +xiiotid,0.25,2,Tampering,Normal,0,0,0,0.0,0.0,0.0 +xiiotid,0.25,2,Tampering,RDOS,0,0,0,0.0,0.0,0.0 +xiiotid,0.25,2,Tampering,Reconnaissance,0,0,0,0.0,0.0,0.0 +xiiotid,0.25,2,Tampering,Tampering,781,779,-2,0.7626953125,0.7607421875,-0.001953125 +xiiotid,0.25,2,Tampering,Weaponization,8,8,0,0.0078125,0.0078125,0.0 +xiiotid,0.25,2,Weaponization,C&C,0,0,0,0.0,0.0,0.0 +xiiotid,0.25,2,Weaponization,Exfiltration,0,0,0,0.0,0.0,0.0 +xiiotid,0.25,2,Weaponization,Exploitation,220,266,46,0.017075442409189692,0.020645762185656627,0.0035703197764669348 +xiiotid,0.25,2,Weaponization,Lateral_movement,0,0,0,0.0,0.0,0.0 +xiiotid,0.25,2,Weaponization,Normal,0,0,0,0.0,0.0,0.0 +xiiotid,0.25,2,Weaponization,RDOS,0,0,0,0.0,0.0,0.0 +xiiotid,0.25,2,Weaponization,Reconnaissance,0,0,0,0.0,0.0,0.0 +xiiotid,0.25,2,Weaponization,Tampering,0,0,0,0.0,0.0,0.0 +xiiotid,0.25,2,Weaponization,Weaponization,12664,12618,-46,0.9829245575908103,0.9793542378143434,-0.0035703197764669348 +xiiotid,0.5,0,C&C,C&C,567,522,-45,0.9895287958115183,0.9109947643979057,-0.07853403141361259 +xiiotid,0.5,0,C&C,Exfiltration,0,0,0,0.0,0.0,0.0 +xiiotid,0.5,0,C&C,Exploitation,0,1,1,0.0,0.0017452006980802793,0.0017452006980802793 +xiiotid,0.5,0,C&C,Lateral_movement,0,0,0,0.0,0.0,0.0 +xiiotid,0.5,0,C&C,Normal,6,50,44,0.010471204188481676,0.08726003490401396,0.07678883071553229 +xiiotid,0.5,0,C&C,RDOS,0,0,0,0.0,0.0,0.0 +xiiotid,0.5,0,C&C,Reconnaissance,0,0,0,0.0,0.0,0.0 +xiiotid,0.5,0,C&C,Tampering,0,0,0,0.0,0.0,0.0 +xiiotid,0.5,0,C&C,Weaponization,0,0,0,0.0,0.0,0.0 +xiiotid,0.5,0,Exfiltration,C&C,1,7,6,0.00022588660492432798,0.001581206234470296,0.001355319629545968 +xiiotid,0.5,0,Exfiltration,Exfiltration,4213,4251,38,0.9516602665461938,0.9602439575333183,0.008583690987124415 +xiiotid,0.5,0,Exfiltration,Exploitation,1,101,100,0.00022588660492432798,0.022814547097357127,0.0225886604924328 +xiiotid,0.5,0,Exfiltration,Lateral_movement,0,0,0,0.0,0.0,0.0 +xiiotid,0.5,0,Exfiltration,Normal,0,0,0,0.0,0.0,0.0 +xiiotid,0.5,0,Exfiltration,RDOS,0,0,0,0.0,0.0,0.0 +xiiotid,0.5,0,Exfiltration,Reconnaissance,0,0,0,0.0,0.0,0.0 +xiiotid,0.5,0,Exfiltration,Tampering,0,0,0,0.0,0.0,0.0 +xiiotid,0.5,0,Exfiltration,Weaponization,212,68,-144,0.047887960243957535,0.015360289134854304,-0.032527671109103234 +xiiotid,0.5,0,Exploitation,C&C,0,0,0,0.0,0.0,0.0 +xiiotid,0.5,0,Exploitation,Exfiltration,0,0,0,0.0,0.0,0.0 +xiiotid,0.5,0,Exploitation,Exploitation,132,132,0,0.5814977973568282,0.5814977973568282,0.0 +xiiotid,0.5,0,Exploitation,Lateral_movement,0,0,0,0.0,0.0,0.0 +xiiotid,0.5,0,Exploitation,Normal,25,25,0,0.11013215859030837,0.11013215859030837,0.0 +xiiotid,0.5,0,Exploitation,RDOS,0,0,0,0.0,0.0,0.0 +xiiotid,0.5,0,Exploitation,Reconnaissance,70,70,0,0.30837004405286345,0.30837004405286345,0.0 +xiiotid,0.5,0,Exploitation,Tampering,0,0,0,0.0,0.0,0.0 +xiiotid,0.5,0,Exploitation,Weaponization,0,0,0,0.0,0.0,0.0 +xiiotid,0.5,0,Lateral_movement,C&C,227,269,42,0.035923405602152236,0.04257002690299098,0.006646621300838741 +xiiotid,0.5,0,Lateral_movement,Exfiltration,0,0,0,0.0,0.0,0.0 +xiiotid,0.5,0,Lateral_movement,Exploitation,692,781,89,0.10951099857572401,0.12359550561797752,0.014084507042253516 +xiiotid,0.5,0,Lateral_movement,Lateral_movement,4593,4929,336,0.7268555151131508,0.7800284855198607,0.053172970406709874 +xiiotid,0.5,0,Lateral_movement,Normal,18,18,0,0.002848551986073746,0.002848551986073746,0.0 +xiiotid,0.5,0,Lateral_movement,RDOS,0,0,0,0.0,0.0,0.0 +xiiotid,0.5,0,Lateral_movement,Reconnaissance,47,7,-40,0.007437885741414781,0.0011077702168064568,-0.0063301155246083245 +xiiotid,0.5,0,Lateral_movement,Tampering,742,315,-427,0.11742364298148442,0.049849659756290556,-0.06757398322519387 +xiiotid,0.5,0,Lateral_movement,Weaponization,0,0,0,0.0,0.0,0.0 +xiiotid,0.5,0,Normal,C&C,5181,5255,74,0.061935900347873905,0.06282052814670476,0.0008846277988308546 +xiiotid,0.5,0,Normal,Exfiltration,0,0,0,0.0,0.0,0.0 +xiiotid,0.5,0,Normal,Exploitation,314,399,85,0.003753690930174176,0.004769817455858269,0.0010161265256840926 +xiiotid,0.5,0,Normal,Lateral_movement,137,136,-1,0.001637756870808478,0.0016258024410945476,-1.1954429713930455e-05 +xiiotid,0.5,0,Normal,Normal,77615,77576,-39,0.9278430622467155,0.9273768394878722,-0.00046622275884333764 +xiiotid,0.5,0,Normal,RDOS,215,211,-4,0.002570202388495057,0.0025223846696393348,-4.7817718855722256e-05 +xiiotid,0.5,0,Normal,Reconnaissance,15,14,-1,0.00017931644570895746,0.00016736201599502695,-1.195442971393051e-05 +xiiotid,0.5,0,Normal,Tampering,47,48,1,0.0005618581965547334,0.0005738126262686638,1.1954429713930455e-05 +xiiotid,0.5,0,Normal,Weaponization,127,12,-115,0.001518212573669173,0.00014345315656716595,-0.0013747594171020072 +xiiotid,0.5,0,RDOS,C&C,24,35,11,0.0008494973807164094,0.001238850346878097,0.0003893529661616877 +xiiotid,0.5,0,RDOS,Exfiltration,0,0,0,0.0,0.0,0.0 +xiiotid,0.5,0,RDOS,Exploitation,0,15,15,0.0,0.000530935862947756,0.000530935862947756 +xiiotid,0.5,0,RDOS,Lateral_movement,0,0,0,0.0,0.0,0.0 +xiiotid,0.5,0,RDOS,Normal,58,36,-22,0.0020529520033979896,0.0012742460710746142,-0.0007787059323233754 +xiiotid,0.5,0,RDOS,RDOS,28170,28166,-4,0.9970975506158856,0.9969559677190996,-0.00014158289678600777 +xiiotid,0.5,0,RDOS,Reconnaissance,0,0,0,0.0,0.0,0.0 +xiiotid,0.5,0,RDOS,Tampering,0,0,0,0.0,0.0,0.0 +xiiotid,0.5,0,RDOS,Weaponization,0,0,0,0.0,0.0,0.0 +xiiotid,0.5,0,Reconnaissance,C&C,144,84,-60,0.005652822485671665,0.0032974797833084713,-0.002355342702363194 +xiiotid,0.5,0,Reconnaissance,Exfiltration,0,0,0,0.0,0.0,0.0 +xiiotid,0.5,0,Reconnaissance,Exploitation,5683,6660,977,0.22309020962550052,0.2614430399623145,0.03835283033681397 +xiiotid,0.5,0,Reconnaissance,Lateral_movement,28,34,6,0.0010991599277694905,0.0013346941980058099,0.00023553427023631935 +xiiotid,0.5,0,Reconnaissance,Normal,2242,2225,-17,0.08801130564497134,0.08734395854596844,-0.0006673470990029018 +xiiotid,0.5,0,Reconnaissance,RDOS,0,0,0,0.0,0.0,0.0 +xiiotid,0.5,0,Reconnaissance,Reconnaissance,15965,16219,254,0.6267174373871398,0.6366883881604773,0.009970950773337539 +xiiotid,0.5,0,Reconnaissance,Tampering,0,0,0,0.0,0.0,0.0 +xiiotid,0.5,0,Reconnaissance,Weaponization,1412,252,-1160,0.05542906492894716,0.009892439349925414,-0.045536625579021744 +xiiotid,0.5,0,Tampering,C&C,3,3,0,0.0029296875,0.0029296875,0.0 +xiiotid,0.5,0,Tampering,Exfiltration,0,0,0,0.0,0.0,0.0 +xiiotid,0.5,0,Tampering,Exploitation,0,0,0,0.0,0.0,0.0 +xiiotid,0.5,0,Tampering,Lateral_movement,5,8,3,0.0048828125,0.0078125,0.0029296875 +xiiotid,0.5,0,Tampering,Normal,0,0,0,0.0,0.0,0.0 +xiiotid,0.5,0,Tampering,RDOS,0,0,0,0.0,0.0,0.0 +xiiotid,0.5,0,Tampering,Reconnaissance,0,0,0,0.0,0.0,0.0 +xiiotid,0.5,0,Tampering,Tampering,1016,1013,-3,0.9921875,0.9892578125,-0.0029296875 +xiiotid,0.5,0,Tampering,Weaponization,0,0,0,0.0,0.0,0.0 +xiiotid,0.5,0,Weaponization,C&C,0,0,0,0.0,0.0,0.0 +xiiotid,0.5,0,Weaponization,Exfiltration,0,0,0,0.0,0.0,0.0 +xiiotid,0.5,0,Weaponization,Exploitation,165,989,824,0.01280658180689227,0.07676187519403911,0.06395529338714684 +xiiotid,0.5,0,Weaponization,Lateral_movement,0,0,0,0.0,0.0,0.0 +xiiotid,0.5,0,Weaponization,Normal,0,0,0,0.0,0.0,0.0 +xiiotid,0.5,0,Weaponization,RDOS,0,0,0,0.0,0.0,0.0 +xiiotid,0.5,0,Weaponization,Reconnaissance,0,0,0,0.0,0.0,0.0 +xiiotid,0.5,0,Weaponization,Tampering,0,0,0,0.0,0.0,0.0 +xiiotid,0.5,0,Weaponization,Weaponization,12719,11895,-824,0.9871934181931077,0.9232381248059609,-0.06395529338714678 +xiiotid,0.5,1,C&C,C&C,563,428,-135,0.9825479930191972,0.7469458987783595,-0.23560209424083767 +xiiotid,0.5,1,C&C,Exfiltration,0,0,0,0.0,0.0,0.0 +xiiotid,0.5,1,C&C,Exploitation,0,0,0,0.0,0.0,0.0 +xiiotid,0.5,1,C&C,Lateral_movement,0,0,0,0.0,0.0,0.0 +xiiotid,0.5,1,C&C,Normal,10,145,135,0.017452006980802792,0.2530541012216405,0.23560209424083772 +xiiotid,0.5,1,C&C,RDOS,0,0,0,0.0,0.0,0.0 +xiiotid,0.5,1,C&C,Reconnaissance,0,0,0,0.0,0.0,0.0 +xiiotid,0.5,1,C&C,Tampering,0,0,0,0.0,0.0,0.0 +xiiotid,0.5,1,C&C,Weaponization,0,0,0,0.0,0.0,0.0 +xiiotid,0.5,1,Exfiltration,C&C,5,5,0,0.00112943302462164,0.00112943302462164,0.0 +xiiotid,0.5,1,Exfiltration,Exfiltration,4323,4229,-94,0.9765077930878698,0.9552744522249831,-0.02123334086288675 +xiiotid,0.5,1,Exfiltration,Exploitation,0,28,28,0.0,0.006324824937881184,0.006324824937881184 +xiiotid,0.5,1,Exfiltration,Lateral_movement,0,0,0,0.0,0.0,0.0 +xiiotid,0.5,1,Exfiltration,Normal,0,0,0,0.0,0.0,0.0 +xiiotid,0.5,1,Exfiltration,RDOS,0,0,0,0.0,0.0,0.0 +xiiotid,0.5,1,Exfiltration,Reconnaissance,1,1,0,0.00022588660492432798,0.00022588660492432798,0.0 +xiiotid,0.5,1,Exfiltration,Tampering,0,0,0,0.0,0.0,0.0 +xiiotid,0.5,1,Exfiltration,Weaponization,98,164,66,0.02213688728258414,0.03704540320758979,0.014908515925005649 +xiiotid,0.5,1,Exploitation,C&C,0,0,0,0.0,0.0,0.0 +xiiotid,0.5,1,Exploitation,Exfiltration,0,0,0,0.0,0.0,0.0 +xiiotid,0.5,1,Exploitation,Exploitation,145,141,-4,0.6387665198237885,0.6211453744493393,-0.017621145374449254 +xiiotid,0.5,1,Exploitation,Lateral_movement,0,0,0,0.0,0.0,0.0 +xiiotid,0.5,1,Exploitation,Normal,19,19,0,0.08370044052863436,0.08370044052863436,0.0 +xiiotid,0.5,1,Exploitation,RDOS,0,0,0,0.0,0.0,0.0 +xiiotid,0.5,1,Exploitation,Reconnaissance,63,67,4,0.2775330396475771,0.29515418502202645,0.017621145374449365 +xiiotid,0.5,1,Exploitation,Tampering,0,0,0,0.0,0.0,0.0 +xiiotid,0.5,1,Exploitation,Weaponization,0,0,0,0.0,0.0,0.0 +xiiotid,0.5,1,Lateral_movement,C&C,9,29,20,0.001424275993036873,0.004589333755341035,0.0031650577623041614 +xiiotid,0.5,1,Lateral_movement,Exfiltration,0,0,0,0.0,0.0,0.0 +xiiotid,0.5,1,Lateral_movement,Exploitation,987,1338,351,0.1561956005697104,0.21174236429814844,0.05554676372843803 +xiiotid,0.5,1,Lateral_movement,Lateral_movement,4263,4246,-17,0.6746320620351322,0.6719417629371736,-0.002690299097958593 +xiiotid,0.5,1,Lateral_movement,Normal,13,14,1,0.0020572875454977053,0.0022155404336129136,0.00015825288811520833 +xiiotid,0.5,1,Lateral_movement,RDOS,0,0,0,0.0,0.0,0.0 +xiiotid,0.5,1,Lateral_movement,Reconnaissance,4,6,2,0.0006330115524608325,0.0009495173286912486,0.0003165057762304161 +xiiotid,0.5,1,Lateral_movement,Tampering,1042,686,-356,0.16489950941604684,0.10856148124703276,-0.05633802816901408 +xiiotid,0.5,1,Lateral_movement,Weaponization,1,0,-1,0.0001582528881152081,0.0,-0.0001582528881152081 +xiiotid,0.5,1,Normal,C&C,5596,3745,-1851,0.06689698867915506,0.04476933927866971,-0.022127649400485346 +xiiotid,0.5,1,Normal,Exfiltration,0,0,0,0.0,0.0,0.0 +xiiotid,0.5,1,Normal,Exploitation,980,794,-186,0.011715341119651886,0.009491817192860814,-0.0022235239267910725 +xiiotid,0.5,1,Normal,Lateral_movement,126,122,-4,0.0015062581439552426,0.0014584404250995206,-4.781771885572204e-05 +xiiotid,0.5,1,Normal,Normal,76633,78778,2145,0.9161038122676358,0.9417460640040167,0.025642251736380905 +xiiotid,0.5,1,Normal,RDOS,145,98,-47,0.0017333923085199221,0.0011715341119651888,-0.0005618581965547334 +xiiotid,0.5,1,Normal,Reconnaissance,24,52,28,0.0002869063131343319,0.0006216303451243859,0.00033472403199005395 +xiiotid,0.5,1,Normal,Tampering,51,54,3,0.0006096759154104554,0.0006455392045522469,3.5863289141791475e-05 +xiiotid,0.5,1,Normal,Weaponization,96,8,-88,0.0011476252525373276,9.563543771144397e-05,-0.0010519898148258836 +xiiotid,0.5,1,RDOS,C&C,28,28,0,0.0009910802775024777,0.0009910802775024777,0.0 +xiiotid,0.5,1,RDOS,Exfiltration,0,0,0,0.0,0.0,0.0 +xiiotid,0.5,1,RDOS,Exploitation,36,0,-36,0.0012742460710746142,0.0,-0.0012742460710746142 +xiiotid,0.5,1,RDOS,Lateral_movement,0,0,0,0.0,0.0,0.0 +xiiotid,0.5,1,RDOS,Normal,26,56,30,0.0009202888291094435,0.0019821605550049554,0.001061871725895512 +xiiotid,0.5,1,RDOS,RDOS,28162,28132,-30,0.9968143848223134,0.995752513096418,-0.0010618717258954469 +xiiotid,0.5,1,RDOS,Reconnaissance,0,36,36,0.0,0.0012742460710746142,0.0012742460710746142 +xiiotid,0.5,1,RDOS,Tampering,0,0,0,0.0,0.0,0.0 +xiiotid,0.5,1,RDOS,Weaponization,0,0,0,0.0,0.0,0.0 +xiiotid,0.5,1,Reconnaissance,C&C,70,54,-16,0.002747899819423726,0.0021198084321268746,-0.0006280913872968516 +xiiotid,0.5,1,Reconnaissance,Exfiltration,1,0,-1,3.925571170605323e-05,0.0,-3.925571170605323e-05 +xiiotid,0.5,1,Reconnaissance,Exploitation,3877,1928,-1949,0.15219439428436837,0.07568501216927064,-0.07650938211509774 +xiiotid,0.5,1,Reconnaissance,Lateral_movement,56,54,-2,0.002198319855538981,0.0021198084321268746,-7.851142341210645e-05 +xiiotid,0.5,1,Reconnaissance,Normal,2098,2113,15,0.08235848315929968,0.08294731883489048,0.0005888356755908014 +xiiotid,0.5,1,Reconnaissance,RDOS,0,0,0,0.0,0.0,0.0 +xiiotid,0.5,1,Reconnaissance,Reconnaissance,18876,21034,2158,0.7409908141634608,0.8257046400251237,0.08471382586166287 +xiiotid,0.5,1,Reconnaissance,Tampering,0,0,0,0.0,0.0,0.0 +xiiotid,0.5,1,Reconnaissance,Weaponization,496,291,-205,0.019470833006202403,0.01142341210646149,-0.008047420899740913 +xiiotid,0.5,1,Tampering,C&C,0,0,0,0.0,0.0,0.0 +xiiotid,0.5,1,Tampering,Exfiltration,0,0,0,0.0,0.0,0.0 +xiiotid,0.5,1,Tampering,Exploitation,4,4,0,0.00390625,0.00390625,0.0 +xiiotid,0.5,1,Tampering,Lateral_movement,23,4,-19,0.0224609375,0.00390625,-0.0185546875 +xiiotid,0.5,1,Tampering,Normal,0,0,0,0.0,0.0,0.0 +xiiotid,0.5,1,Tampering,RDOS,0,0,0,0.0,0.0,0.0 +xiiotid,0.5,1,Tampering,Reconnaissance,0,0,0,0.0,0.0,0.0 +xiiotid,0.5,1,Tampering,Tampering,991,1010,19,0.9677734375,0.986328125,0.0185546875 +xiiotid,0.5,1,Tampering,Weaponization,6,6,0,0.005859375,0.005859375,0.0 +xiiotid,0.5,1,Weaponization,C&C,0,0,0,0.0,0.0,0.0 +xiiotid,0.5,1,Weaponization,Exfiltration,0,0,0,0.0,0.0,0.0 +xiiotid,0.5,1,Weaponization,Exploitation,147,182,35,0.011409500155231294,0.014126047811238746,0.002716547656007451 +xiiotid,0.5,1,Weaponization,Lateral_movement,0,0,0,0.0,0.0,0.0 +xiiotid,0.5,1,Weaponization,Normal,1,1,0,7.76156473144986e-05,7.76156473144986e-05,0.0 +xiiotid,0.5,1,Weaponization,RDOS,0,0,0,0.0,0.0,0.0 +xiiotid,0.5,1,Weaponization,Reconnaissance,0,0,0,0.0,0.0,0.0 +xiiotid,0.5,1,Weaponization,Tampering,0,0,0,0.0,0.0,0.0 +xiiotid,0.5,1,Weaponization,Weaponization,12736,12701,-35,0.9885128841974542,0.9857963365414467,-0.002716547656007484 +xiiotid,0.5,2,C&C,C&C,569,569,0,0.9930191972076788,0.9930191972076788,0.0 +xiiotid,0.5,2,C&C,Exfiltration,0,0,0,0.0,0.0,0.0 +xiiotid,0.5,2,C&C,Exploitation,1,2,1,0.0017452006980802793,0.0034904013961605585,0.0017452006980802793 +xiiotid,0.5,2,C&C,Lateral_movement,0,0,0,0.0,0.0,0.0 +xiiotid,0.5,2,C&C,Normal,3,2,-1,0.005235602094240838,0.0034904013961605585,-0.0017452006980802795 +xiiotid,0.5,2,C&C,RDOS,0,0,0,0.0,0.0,0.0 +xiiotid,0.5,2,C&C,Reconnaissance,0,0,0,0.0,0.0,0.0 +xiiotid,0.5,2,C&C,Tampering,0,0,0,0.0,0.0,0.0 +xiiotid,0.5,2,C&C,Weaponization,0,0,0,0.0,0.0,0.0 +xiiotid,0.5,2,Exfiltration,C&C,0,0,0,0.0,0.0,0.0 +xiiotid,0.5,2,Exfiltration,Exfiltration,4234,4249,15,0.9564038852496047,0.9597921843234696,0.003388299073864842 +xiiotid,0.5,2,Exfiltration,Exploitation,0,177,177,0.0,0.039981929071606054,0.039981929071606054 +xiiotid,0.5,2,Exfiltration,Lateral_movement,0,0,0,0.0,0.0,0.0 +xiiotid,0.5,2,Exfiltration,Normal,0,0,0,0.0,0.0,0.0 +xiiotid,0.5,2,Exfiltration,RDOS,0,0,0,0.0,0.0,0.0 +xiiotid,0.5,2,Exfiltration,Reconnaissance,0,0,0,0.0,0.0,0.0 +xiiotid,0.5,2,Exfiltration,Tampering,0,0,0,0.0,0.0,0.0 +xiiotid,0.5,2,Exfiltration,Weaponization,193,1,-192,0.0435961147503953,0.00022588660492432798,-0.04337022814547097 +xiiotid,0.5,2,Exploitation,C&C,0,0,0,0.0,0.0,0.0 +xiiotid,0.5,2,Exploitation,Exfiltration,0,0,0,0.0,0.0,0.0 +xiiotid,0.5,2,Exploitation,Exploitation,140,138,-2,0.6167400881057269,0.6079295154185022,-0.008810572687224738 +xiiotid,0.5,2,Exploitation,Lateral_movement,0,0,0,0.0,0.0,0.0 +xiiotid,0.5,2,Exploitation,Normal,22,22,0,0.09691629955947137,0.09691629955947137,0.0 +xiiotid,0.5,2,Exploitation,RDOS,0,0,0,0.0,0.0,0.0 +xiiotid,0.5,2,Exploitation,Reconnaissance,65,67,2,0.28634361233480177,0.29515418502202645,0.008810572687224683 +xiiotid,0.5,2,Exploitation,Tampering,0,0,0,0.0,0.0,0.0 +xiiotid,0.5,2,Exploitation,Weaponization,0,0,0,0.0,0.0,0.0 +xiiotid,0.5,2,Lateral_movement,C&C,1,0,-1,0.0001582528881152081,0.0,-0.0001582528881152081 +xiiotid,0.5,2,Lateral_movement,Exfiltration,0,0,0,0.0,0.0,0.0 +xiiotid,0.5,2,Lateral_movement,Exploitation,329,418,89,0.05206520018990347,0.06614970723215699,0.014084507042253523 +xiiotid,0.5,2,Lateral_movement,Lateral_movement,4627,5432,805,0.7322361133090679,0.8596296882418104,0.1273935749327425 +xiiotid,0.5,2,Lateral_movement,Normal,60,13,-47,0.009495173286912486,0.0020572875454977053,-0.00743788574141478 +xiiotid,0.5,2,Lateral_movement,RDOS,0,0,0,0.0,0.0,0.0 +xiiotid,0.5,2,Lateral_movement,Reconnaissance,265,291,26,0.04193701535053015,0.046051590441525556,0.004114575090995408 +xiiotid,0.5,2,Lateral_movement,Tampering,1037,165,-872,0.1641082449754708,0.026111726539009335,-0.13799651843646146 +xiiotid,0.5,2,Lateral_movement,Weaponization,0,0,0,0.0,0.0,0.0 +xiiotid,0.5,2,Normal,C&C,2657,7351,4694,0.03176291974991333,0.08787701282710308,0.05611409307718975 +xiiotid,0.5,2,Normal,Exfiltration,1,1,0,1.1954429713930496e-05,1.1954429713930496e-05,0.0 +xiiotid,0.5,2,Normal,Exploitation,1086,1359,273,0.012982510669328519,0.016246069981231546,0.003263559311903027 +xiiotid,0.5,2,Normal,Lateral_movement,144,138,-6,0.0017214378788059917,0.0016497113005224085,-7.172657828358317e-05 +xiiotid,0.5,2,Normal,Normal,79415,74398,-5017,0.9493610357317904,0.8893856618570011,-0.05997537387478935 +xiiotid,0.5,2,Normal,RDOS,211,153,-58,0.0025223846696393348,0.001829027746231366,-0.0006933569234079688 +xiiotid,0.5,2,Normal,Reconnaissance,39,207,168,0.0004662227588432894,0.002474566950783613,0.0020083441919403235 +xiiotid,0.5,2,Normal,Tampering,36,35,-1,0.0004303594697014979,0.0004184050399875674,-1.195442971393051e-05 +xiiotid,0.5,2,Normal,Weaponization,62,9,-53,0.0007411746422636908,0.00010758986742537448,-0.0006335847748383163 +xiiotid,0.5,2,RDOS,C&C,4,12,8,0.00014158289678606824,0.0004247486903582047,0.00028316579357213643 +xiiotid,0.5,2,RDOS,Exfiltration,0,0,0,0.0,0.0,0.0 +xiiotid,0.5,2,RDOS,Exploitation,22,0,-22,0.0007787059323233754,0.0,-0.0007787059323233754 +xiiotid,0.5,2,RDOS,Lateral_movement,0,0,0,0.0,0.0,0.0 +xiiotid,0.5,2,RDOS,Normal,54,23,-31,0.0019113691066119213,0.0008141016565198924,-0.001097267450092029 +xiiotid,0.5,2,RDOS,RDOS,28172,28161,-11,0.9971683420642786,0.9967789890981169,-0.00038935296616171566 +xiiotid,0.5,2,RDOS,Reconnaissance,0,56,56,0.0,0.0019821605550049554,0.0019821605550049554 +xiiotid,0.5,2,RDOS,Tampering,0,0,0,0.0,0.0,0.0 +xiiotid,0.5,2,RDOS,Weaponization,0,0,0,0.0,0.0,0.0 +xiiotid,0.5,2,Reconnaissance,C&C,29,69,40,0.0011384156394755437,0.002708644107717673,0.0015702284682421292 +xiiotid,0.5,2,Reconnaissance,Exfiltration,0,0,0,0.0,0.0,0.0 +xiiotid,0.5,2,Reconnaissance,Exploitation,5000,1821,-3179,0.19627855853026616,0.07148465101672294,-0.12479390751354322 +xiiotid,0.5,2,Reconnaissance,Lateral_movement,40,40,0,0.0015702284682421292,0.0015702284682421292,0.0 +xiiotid,0.5,2,Reconnaissance,Normal,2277,2187,-90,0.08938525555468321,0.08585224150113842,-0.0035330140535447946 +xiiotid,0.5,2,Reconnaissance,RDOS,0,0,0,0.0,0.0,0.0 +xiiotid,0.5,2,Reconnaissance,Reconnaissance,17030,21217,4187,0.6685247703540865,0.8328884352673314,0.16436366491324483 +xiiotid,0.5,2,Reconnaissance,Tampering,0,0,0,0.0,0.0,0.0 +xiiotid,0.5,2,Reconnaissance,Weaponization,1098,140,-958,0.043102771453246445,0.005495799638847452,-0.037606971814398996 +xiiotid,0.5,2,Tampering,C&C,0,0,0,0.0,0.0,0.0 +xiiotid,0.5,2,Tampering,Exfiltration,0,0,0,0.0,0.0,0.0 +xiiotid,0.5,2,Tampering,Exploitation,1,3,2,0.0009765625,0.0029296875,0.001953125 +xiiotid,0.5,2,Tampering,Lateral_movement,191,230,39,0.1865234375,0.224609375,0.0380859375 +xiiotid,0.5,2,Tampering,Normal,2,0,-2,0.001953125,0.0,-0.001953125 +xiiotid,0.5,2,Tampering,RDOS,0,0,0,0.0,0.0,0.0 +xiiotid,0.5,2,Tampering,Reconnaissance,0,0,0,0.0,0.0,0.0 +xiiotid,0.5,2,Tampering,Tampering,830,786,-44,0.810546875,0.767578125,-0.04296875 +xiiotid,0.5,2,Tampering,Weaponization,0,5,5,0.0,0.0048828125,0.0048828125 +xiiotid,0.5,2,Weaponization,C&C,0,0,0,0.0,0.0,0.0 +xiiotid,0.5,2,Weaponization,Exfiltration,0,0,0,0.0,0.0,0.0 +xiiotid,0.5,2,Weaponization,Exploitation,206,466,260,0.015988823346786713,0.03616889164855635,0.020180068301769636 +xiiotid,0.5,2,Weaponization,Lateral_movement,0,0,0,0.0,0.0,0.0 +xiiotid,0.5,2,Weaponization,Normal,0,0,0,0.0,0.0,0.0 +xiiotid,0.5,2,Weaponization,RDOS,0,0,0,0.0,0.0,0.0 +xiiotid,0.5,2,Weaponization,Reconnaissance,0,4,4,0.0,0.0003104625892579944,0.0003104625892579944 +xiiotid,0.5,2,Weaponization,Tampering,0,0,0,0.0,0.0,0.0 +xiiotid,0.5,2,Weaponization,Weaponization,12678,12414,-264,0.9840111766532132,0.9635206457621857,-0.020490530891027592 +xiiotid,0.75,0,C&C,C&C,553,561,8,0.9650959860383944,0.9790575916230366,0.013961605584642212 +xiiotid,0.75,0,C&C,Exfiltration,0,0,0,0.0,0.0,0.0 +xiiotid,0.75,0,C&C,Exploitation,0,0,0,0.0,0.0,0.0 +xiiotid,0.75,0,C&C,Lateral_movement,0,0,0,0.0,0.0,0.0 +xiiotid,0.75,0,C&C,Normal,20,12,-8,0.034904013961605584,0.020942408376963352,-0.013961605584642232 +xiiotid,0.75,0,C&C,RDOS,0,0,0,0.0,0.0,0.0 +xiiotid,0.75,0,C&C,Reconnaissance,0,0,0,0.0,0.0,0.0 +xiiotid,0.75,0,C&C,Tampering,0,0,0,0.0,0.0,0.0 +xiiotid,0.75,0,C&C,Weaponization,0,0,0,0.0,0.0,0.0 +xiiotid,0.75,0,Exfiltration,C&C,0,0,0,0.0,0.0,0.0 +xiiotid,0.75,0,Exfiltration,Exfiltration,4217,3894,-323,0.9525638129658911,0.8796024395753331,-0.07296137339055797 +xiiotid,0.75,0,Exfiltration,Exploitation,1,2,1,0.00022588660492432798,0.00045177320984865596,0.00022588660492432798 +xiiotid,0.75,0,Exfiltration,Lateral_movement,0,0,0,0.0,0.0,0.0 +xiiotid,0.75,0,Exfiltration,Normal,0,0,0,0.0,0.0,0.0 +xiiotid,0.75,0,Exfiltration,RDOS,0,0,0,0.0,0.0,0.0 +xiiotid,0.75,0,Exfiltration,Reconnaissance,0,0,0,0.0,0.0,0.0 +xiiotid,0.75,0,Exfiltration,Tampering,0,0,0,0.0,0.0,0.0 +xiiotid,0.75,0,Exfiltration,Weaponization,209,531,322,0.04721030042918455,0.11994578721481816,0.0727354867856336 +xiiotid,0.75,0,Exploitation,C&C,0,0,0,0.0,0.0,0.0 +xiiotid,0.75,0,Exploitation,Exfiltration,0,0,0,0.0,0.0,0.0 +xiiotid,0.75,0,Exploitation,Exploitation,132,132,0,0.5814977973568282,0.5814977973568282,0.0 +xiiotid,0.75,0,Exploitation,Lateral_movement,0,0,0,0.0,0.0,0.0 +xiiotid,0.75,0,Exploitation,Normal,25,25,0,0.11013215859030837,0.11013215859030837,0.0 +xiiotid,0.75,0,Exploitation,RDOS,0,0,0,0.0,0.0,0.0 +xiiotid,0.75,0,Exploitation,Reconnaissance,70,70,0,0.30837004405286345,0.30837004405286345,0.0 +xiiotid,0.75,0,Exploitation,Tampering,0,0,0,0.0,0.0,0.0 +xiiotid,0.75,0,Exploitation,Weaponization,0,0,0,0.0,0.0,0.0 +xiiotid,0.75,0,Lateral_movement,C&C,122,229,107,0.019306852350055387,0.03623991137838266,0.01693305902832727 +xiiotid,0.75,0,Lateral_movement,Exfiltration,0,0,0,0.0,0.0,0.0 +xiiotid,0.75,0,Lateral_movement,Exploitation,292,407,115,0.046209843329640767,0.0644089254628897,0.018199082133248938 +xiiotid,0.75,0,Lateral_movement,Lateral_movement,5597,5606,9,0.8857414147808198,0.8871656907738567,0.0014242759930368498 +xiiotid,0.75,0,Lateral_movement,Normal,20,19,-1,0.003165057762304162,0.003006804874188954,-0.0001582528881152079 +xiiotid,0.75,0,Lateral_movement,RDOS,0,0,0,0.0,0.0,0.0 +xiiotid,0.75,0,Lateral_movement,Reconnaissance,164,5,-159,0.025953473650894128,0.0007912644405760405,-0.025162209210318087 +xiiotid,0.75,0,Lateral_movement,Tampering,124,53,-71,0.019623358126285805,0.00838740307010603,-0.011235955056179775 +xiiotid,0.75,0,Lateral_movement,Weaponization,0,0,0,0.0,0.0,0.0 +xiiotid,0.75,0,Normal,C&C,2412,2898,486,0.028834084470000357,0.03464393731097058,0.005809852840970225 +xiiotid,0.75,0,Normal,Exfiltration,1,1,0,1.1954429713930496e-05,1.1954429713930496e-05,0.0 +xiiotid,0.75,0,Normal,Exploitation,673,529,-144,0.008045331197475225,0.006323893318669233,-0.0017214378788059917 +xiiotid,0.75,0,Normal,Lateral_movement,135,142,7,0.0016138480113806171,0.0016975290193781305,8.36810079975134e-05 +xiiotid,0.75,0,Normal,Normal,80010,79729,-281,0.956473921411579,0.9531147266619646,-0.003359194749614436 +xiiotid,0.75,0,Normal,RDOS,224,224,0,0.002677792255920431,0.002677792255920431,0.0 +xiiotid,0.75,0,Normal,Reconnaissance,44,23,-21,0.0005259949074129419,0.00027495188342040145,-0.00025104302399254043 +xiiotid,0.75,0,Normal,Tampering,72,68,-4,0.0008607189394029958,0.0008129012205472738,-4.781771885572204e-05 +xiiotid,0.75,0,Normal,Weaponization,80,37,-43,0.0009563543771144398,0.00044231389941542837,-0.0005140404776990114 +xiiotid,0.75,0,RDOS,C&C,24,40,16,0.0008494973807164094,0.0014158289678606825,0.0005663315871442731 +xiiotid,0.75,0,RDOS,Exfiltration,0,0,0,0.0,0.0,0.0 +xiiotid,0.75,0,RDOS,Exploitation,0,0,0,0.0,0.0,0.0 +xiiotid,0.75,0,RDOS,Lateral_movement,0,0,0,0.0,0.0,0.0 +xiiotid,0.75,0,RDOS,Normal,58,42,-16,0.0020529520033979896,0.0014866204162537165,-0.0005663315871442731 +xiiotid,0.75,0,RDOS,RDOS,28170,28170,0,0.9970975506158856,0.9970975506158856,0.0 +xiiotid,0.75,0,RDOS,Reconnaissance,0,0,0,0.0,0.0,0.0 +xiiotid,0.75,0,RDOS,Tampering,0,0,0,0.0,0.0,0.0 +xiiotid,0.75,0,RDOS,Weaponization,0,0,0,0.0,0.0,0.0 +xiiotid,0.75,0,Reconnaissance,C&C,124,110,-14,0.004867708251550601,0.004318128287665856,-0.0005495799638847451 +xiiotid,0.75,0,Reconnaissance,Exfiltration,1,0,-1,3.925571170605323e-05,0.0,-3.925571170605323e-05 +xiiotid,0.75,0,Reconnaissance,Exploitation,5634,5438,-196,0.2211666797519039,0.21347256025751746,-0.007694119494386448 +xiiotid,0.75,0,Reconnaissance,Lateral_movement,44,48,4,0.0017272513150663421,0.001884274161890555,0.0001570228468242129 +xiiotid,0.75,0,Reconnaissance,Normal,2256,2249,-7,0.08856088560885608,0.08828609562691371,-0.00027478998194237214 +xiiotid,0.75,0,Reconnaissance,RDOS,0,0,0,0.0,0.0,0.0 +xiiotid,0.75,0,Reconnaissance,Reconnaissance,16453,17239,786,0.6458742246996938,0.6767292141006517,0.030854989400957877 +xiiotid,0.75,0,Reconnaissance,Tampering,0,0,0,0.0,0.0,0.0 +xiiotid,0.75,0,Reconnaissance,Weaponization,962,390,-572,0.03776399466122321,0.01530972756536076,-0.02245426709586245 +xiiotid,0.75,0,Tampering,C&C,3,2,-1,0.0029296875,0.001953125,-0.0009765625 +xiiotid,0.75,0,Tampering,Exfiltration,0,0,0,0.0,0.0,0.0 +xiiotid,0.75,0,Tampering,Exploitation,0,1,1,0.0,0.0009765625,0.0009765625 +xiiotid,0.75,0,Tampering,Lateral_movement,229,220,-9,0.2236328125,0.21484375,-0.0087890625 +xiiotid,0.75,0,Tampering,Normal,0,0,0,0.0,0.0,0.0 +xiiotid,0.75,0,Tampering,RDOS,0,0,0,0.0,0.0,0.0 +xiiotid,0.75,0,Tampering,Reconnaissance,0,0,0,0.0,0.0,0.0 +xiiotid,0.75,0,Tampering,Tampering,785,794,9,0.7666015625,0.775390625,0.0087890625 +xiiotid,0.75,0,Tampering,Weaponization,7,7,0,0.0068359375,0.0068359375,0.0 +xiiotid,0.75,0,Weaponization,C&C,0,0,0,0.0,0.0,0.0 +xiiotid,0.75,0,Weaponization,Exfiltration,0,0,0,0.0,0.0,0.0 +xiiotid,0.75,0,Weaponization,Exploitation,95,122,27,0.007373486494877368,0.00946910897236883,0.0020956224774914623 +xiiotid,0.75,0,Weaponization,Lateral_movement,0,0,0,0.0,0.0,0.0 +xiiotid,0.75,0,Weaponization,Normal,0,0,0,0.0,0.0,0.0 +xiiotid,0.75,0,Weaponization,RDOS,0,0,0,0.0,0.0,0.0 +xiiotid,0.75,0,Weaponization,Reconnaissance,0,0,0,0.0,0.0,0.0 +xiiotid,0.75,0,Weaponization,Tampering,0,0,0,0.0,0.0,0.0 +xiiotid,0.75,0,Weaponization,Weaponization,12789,12762,-27,0.9926265135051227,0.9905308910276311,-0.00209562247749151 +xiiotid,0.75,1,C&C,C&C,566,565,-1,0.987783595113438,0.9860383944153578,-0.0017452006980802626 +xiiotid,0.75,1,C&C,Exfiltration,0,0,0,0.0,0.0,0.0 +xiiotid,0.75,1,C&C,Exploitation,0,0,0,0.0,0.0,0.0 +xiiotid,0.75,1,C&C,Lateral_movement,0,0,0,0.0,0.0,0.0 +xiiotid,0.75,1,C&C,Normal,7,8,1,0.012216404886561954,0.013961605584642234,0.00174520069808028 +xiiotid,0.75,1,C&C,RDOS,0,0,0,0.0,0.0,0.0 +xiiotid,0.75,1,C&C,Reconnaissance,0,0,0,0.0,0.0,0.0 +xiiotid,0.75,1,C&C,Tampering,0,0,0,0.0,0.0,0.0 +xiiotid,0.75,1,C&C,Weaponization,0,0,0,0.0,0.0,0.0 +xiiotid,0.75,1,Exfiltration,C&C,1,1,0,0.00022588660492432798,0.00022588660492432798,0.0 +xiiotid,0.75,1,Exfiltration,Exfiltration,4251,4252,1,0.9602439575333183,0.9604698441382427,0.00022588660492439683 +xiiotid,0.75,1,Exfiltration,Exploitation,0,0,0,0.0,0.0,0.0 +xiiotid,0.75,1,Exfiltration,Lateral_movement,0,0,0,0.0,0.0,0.0 +xiiotid,0.75,1,Exfiltration,Normal,0,0,0,0.0,0.0,0.0 +xiiotid,0.75,1,Exfiltration,RDOS,0,0,0,0.0,0.0,0.0 +xiiotid,0.75,1,Exfiltration,Reconnaissance,1,1,0,0.00022588660492432798,0.00022588660492432798,0.0 +xiiotid,0.75,1,Exfiltration,Tampering,0,0,0,0.0,0.0,0.0 +xiiotid,0.75,1,Exfiltration,Weaponization,174,173,-1,0.03930426925683307,0.039078382651908744,-0.00022588660492432744 +xiiotid,0.75,1,Exploitation,C&C,0,0,0,0.0,0.0,0.0 +xiiotid,0.75,1,Exploitation,Exfiltration,0,0,0,0.0,0.0,0.0 +xiiotid,0.75,1,Exploitation,Exploitation,144,144,0,0.6343612334801763,0.6343612334801763,0.0 +xiiotid,0.75,1,Exploitation,Lateral_movement,0,0,0,0.0,0.0,0.0 +xiiotid,0.75,1,Exploitation,Normal,19,19,0,0.08370044052863436,0.08370044052863436,0.0 +xiiotid,0.75,1,Exploitation,RDOS,0,0,0,0.0,0.0,0.0 +xiiotid,0.75,1,Exploitation,Reconnaissance,64,64,0,0.28193832599118945,0.28193832599118945,0.0 +xiiotid,0.75,1,Exploitation,Tampering,0,0,0,0.0,0.0,0.0 +xiiotid,0.75,1,Exploitation,Weaponization,0,0,0,0.0,0.0,0.0 +xiiotid,0.75,1,Lateral_movement,C&C,130,128,-2,0.020572875454977053,0.02025636967874664,-0.0003165057762304149 +xiiotid,0.75,1,Lateral_movement,Exfiltration,0,0,0,0.0,0.0,0.0 +xiiotid,0.75,1,Lateral_movement,Exploitation,286,290,4,0.045260326000949515,0.04589333755341035,0.0006330115524608368 +xiiotid,0.75,1,Lateral_movement,Lateral_movement,5758,5803,45,0.9112201297673682,0.9183415097325526,0.00712137996518436 +xiiotid,0.75,1,Lateral_movement,Normal,13,13,0,0.0020572875454977053,0.0020572875454977053,0.0 +xiiotid,0.75,1,Lateral_movement,RDOS,0,0,0,0.0,0.0,0.0 +xiiotid,0.75,1,Lateral_movement,Reconnaissance,59,50,-9,0.009336920398797278,0.007912644405760404,-0.001424275993036874 +xiiotid,0.75,1,Lateral_movement,Tampering,73,35,-38,0.011552460832410192,0.005538851084032284,-0.006013609748377908 +xiiotid,0.75,1,Lateral_movement,Weaponization,0,0,0,0.0,0.0,0.0 +xiiotid,0.75,1,Normal,C&C,4336,3885,-451,0.051834407239602635,0.04644295943861998,-0.005391447800982656 +xiiotid,0.75,1,Normal,Exfiltration,0,0,0,0.0,0.0,0.0 +xiiotid,0.75,1,Normal,Exploitation,361,411,50,0.00431554912672891,0.004913270612425434,0.0005977214856965245 +xiiotid,0.75,1,Normal,Lateral_movement,114,112,-2,0.0013628049873880767,0.0013388961279602156,-2.3908859427861128e-05 +xiiotid,0.75,1,Normal,Normal,78509,78901,392,0.9385303224109693,0.9432164588588301,0.004686136447860756 +xiiotid,0.75,1,Normal,RDOS,175,193,18,0.002092025199937837,0.0023072049347885857,0.00021517973485074863 +xiiotid,0.75,1,Normal,Reconnaissance,24,53,29,0.0002869063131343319,0.0006335847748383163,0.0003466784617039844 +xiiotid,0.75,1,Normal,Tampering,70,77,7,0.0008368100799751348,0.0009204910879726482,8.36810079975134e-05 +xiiotid,0.75,1,Normal,Weaponization,62,19,-43,0.0007411746422636908,0.00022713416456467944,-0.0005140404776990114 +xiiotid,0.75,1,RDOS,C&C,35,3,-32,0.001238850346878097,0.00010618717258955118,-0.001132663174288546 +xiiotid,0.75,1,RDOS,Exfiltration,0,0,0,0.0,0.0,0.0 +xiiotid,0.75,1,RDOS,Exploitation,17,0,-17,0.00060172731134079,0.0,-0.00060172731134079 +xiiotid,0.75,1,RDOS,Lateral_movement,0,0,0,0.0,0.0,0.0 +xiiotid,0.75,1,RDOS,Normal,36,62,26,0.0012742460710746142,0.0021945349001840577,0.0009202888291094434 +xiiotid,0.75,1,RDOS,RDOS,28164,28170,6,0.9968851762707065,0.9970975506158856,0.00021237434517906717 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+xiiotid,0.75,1,Reconnaissance,Weaponization,887,698,-189,0.03481981628326922,0.027400486770825155,-0.007419329512444062 +xiiotid,0.75,1,Tampering,C&C,4,1,-3,0.00390625,0.0009765625,-0.0029296875 +xiiotid,0.75,1,Tampering,Exfiltration,0,0,0,0.0,0.0,0.0 +xiiotid,0.75,1,Tampering,Exploitation,0,3,3,0.0,0.0029296875,0.0029296875 +xiiotid,0.75,1,Tampering,Lateral_movement,157,189,32,0.1533203125,0.1845703125,0.03125 +xiiotid,0.75,1,Tampering,Normal,0,0,0,0.0,0.0,0.0 +xiiotid,0.75,1,Tampering,RDOS,0,0,0,0.0,0.0,0.0 +xiiotid,0.75,1,Tampering,Reconnaissance,0,0,0,0.0,0.0,0.0 +xiiotid,0.75,1,Tampering,Tampering,857,825,-32,0.8369140625,0.8056640625,-0.03125 +xiiotid,0.75,1,Tampering,Weaponization,6,6,0,0.005859375,0.005859375,0.0 +xiiotid,0.75,1,Weaponization,C&C,1,0,-1,7.76156473144986e-05,0.0,-7.76156473144986e-05 +xiiotid,0.75,1,Weaponization,Exfiltration,0,0,0,0.0,0.0,0.0 +xiiotid,0.75,1,Weaponization,Exploitation,112,142,30,0.008692952499223843,0.011021421918658802,0.0023284694194349587 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+xiiotid,0.75,2,C&C,Weaponization,0,0,0,0.0,0.0,0.0 +xiiotid,0.75,2,Exfiltration,C&C,0,0,0,0.0,0.0,0.0 +xiiotid,0.75,2,Exfiltration,Exfiltration,4243,4243,0,0.9584368646939236,0.9584368646939236,0.0 +xiiotid,0.75,2,Exfiltration,Exploitation,0,0,0,0.0,0.0,0.0 +xiiotid,0.75,2,Exfiltration,Lateral_movement,0,0,0,0.0,0.0,0.0 +xiiotid,0.75,2,Exfiltration,Normal,0,0,0,0.0,0.0,0.0 +xiiotid,0.75,2,Exfiltration,RDOS,0,0,0,0.0,0.0,0.0 +xiiotid,0.75,2,Exfiltration,Reconnaissance,0,0,0,0.0,0.0,0.0 +xiiotid,0.75,2,Exfiltration,Tampering,0,0,0,0.0,0.0,0.0 +xiiotid,0.75,2,Exfiltration,Weaponization,184,184,0,0.04156313530607635,0.04156313530607635,0.0 +xiiotid,0.75,2,Exploitation,C&C,0,0,0,0.0,0.0,0.0 +xiiotid,0.75,2,Exploitation,Exfiltration,0,0,0,0.0,0.0,0.0 +xiiotid,0.75,2,Exploitation,Exploitation,140,140,0,0.6167400881057269,0.6167400881057269,0.0 +xiiotid,0.75,2,Exploitation,Lateral_movement,0,0,0,0.0,0.0,0.0 +xiiotid,0.75,2,Exploitation,Normal,22,22,0,0.09691629955947137,0.09691629955947137,0.0 +xiiotid,0.75,2,Exploitation,RDOS,0,0,0,0.0,0.0,0.0 +xiiotid,0.75,2,Exploitation,Reconnaissance,65,65,0,0.28634361233480177,0.28634361233480177,0.0 +xiiotid,0.75,2,Exploitation,Tampering,0,0,0,0.0,0.0,0.0 +xiiotid,0.75,2,Exploitation,Weaponization,0,0,0,0.0,0.0,0.0 +xiiotid,0.75,2,Lateral_movement,C&C,51,48,-3,0.008070897293875614,0.007596138629529989,-0.000474758664345625 +xiiotid,0.75,2,Lateral_movement,Exfiltration,0,0,0,0.0,0.0,0.0 +xiiotid,0.75,2,Lateral_movement,Exploitation,360,510,150,0.056971039721474916,0.08070897293875613,0.023737933217281217 +xiiotid,0.75,2,Lateral_movement,Lateral_movement,5752,5556,-196,0.910270612438677,0.8792530463680962,-0.031017566070580815 +xiiotid,0.75,2,Lateral_movement,Normal,13,13,0,0.0020572875454977053,0.0020572875454977053,0.0 +xiiotid,0.75,2,Lateral_movement,RDOS,0,0,0,0.0,0.0,0.0 +xiiotid,0.75,2,Lateral_movement,Reconnaissance,73,57,-16,0.011552460832410192,0.009020414622566862,-0.00253204620984333 +xiiotid,0.75,2,Lateral_movement,Tampering,70,135,65,0.011077702168064568,0.021364139895553094,0.010286437727488527 +xiiotid,0.75,2,Lateral_movement,Weaponization,0,0,0,0.0,0.0,0.0 +xiiotid,0.75,2,Normal,C&C,2374,1968,-406,0.028379816140871,0.02352631767701522,-0.004853498463855781 +xiiotid,0.75,2,Normal,Exfiltration,1,1,0,1.1954429713930496e-05,1.1954429713930496e-05,0.0 +xiiotid,0.75,2,Normal,Exploitation,962,972,10,0.011500161384801137,0.011619705681940443,0.00011954429713930542 +xiiotid,0.75,2,Normal,Lateral_movement,121,123,2,0.00144648599538559,0.0014703948548134512,2.3908859427861128e-05 +xiiotid,0.75,2,Normal,Normal,79795,80256,461,0.953903719023084,0.9594147111212059,0.005510992098121892 +xiiotid,0.75,2,Normal,RDOS,229,230,1,0.002737564404490084,0.0027495188342040143,1.1954429713930455e-05 +xiiotid,0.75,2,Normal,Reconnaissance,21,24,3,0.00025104302399254043,0.0002869063131343319,3.5863289141791475e-05 +xiiotid,0.75,2,Normal,Tampering,47,43,-4,0.0005618581965547334,0.0005140404776990113,-4.781771885572204e-05 +xiiotid,0.75,2,Normal,Weaponization,101,34,-67,0.0012073974011069801,0.0004064506102736369,-0.0008009467908333432 +xiiotid,0.75,2,RDOS,C&C,0,0,0,0.0,0.0,0.0 +xiiotid,0.75,2,RDOS,Exfiltration,0,0,0,0.0,0.0,0.0 +xiiotid,0.75,2,RDOS,Exploitation,22,22,0,0.0007787059323233754,0.0007787059323233754,0.0 +xiiotid,0.75,2,RDOS,Lateral_movement,0,0,0,0.0,0.0,0.0 +xiiotid,0.75,2,RDOS,Normal,54,54,0,0.0019113691066119213,0.0019113691066119213,0.0 +xiiotid,0.75,2,RDOS,RDOS,28176,28176,0,0.9973099249610647,0.9973099249610647,0.0 +xiiotid,0.75,2,RDOS,Reconnaissance,0,0,0,0.0,0.0,0.0 +xiiotid,0.75,2,RDOS,Tampering,0,0,0,0.0,0.0,0.0 +xiiotid,0.75,2,RDOS,Weaponization,0,0,0,0.0,0.0,0.0 +xiiotid,0.75,2,Reconnaissance,C&C,32,36,4,0.0012561827745937034,0.0014132056214179163,0.0001570228468242129 +xiiotid,0.75,2,Reconnaissance,Exfiltration,0,0,0,0.0,0.0,0.0 +xiiotid,0.75,2,Reconnaissance,Exploitation,2475,2522,47,0.09715788647248175,0.09900290492266625,0.0018450184501845046 +xiiotid,0.75,2,Reconnaissance,Lateral_movement,44,40,-4,0.0017272513150663421,0.0015702284682421292,-0.0001570228468242129 +xiiotid,0.75,2,Reconnaissance,Normal,2252,2248,-4,0.08840386276203188,0.08824683991520767,-0.00015702284682421463 +xiiotid,0.75,2,Reconnaissance,RDOS,0,0,0,0.0,0.0,0.0 +xiiotid,0.75,2,Reconnaissance,Reconnaissance,20041,20312,271,0.7867237183010128,0.7973620161733532,0.010638297872340385 +xiiotid,0.75,2,Reconnaissance,Tampering,0,0,0,0.0,0.0,0.0 +xiiotid,0.75,2,Reconnaissance,Weaponization,630,316,-314,0.024731098374813534,0.01240480489911282,-0.012326293475700714 +xiiotid,0.75,2,Tampering,C&C,2,2,0,0.001953125,0.001953125,0.0 +xiiotid,0.75,2,Tampering,Exfiltration,0,0,0,0.0,0.0,0.0 +xiiotid,0.75,2,Tampering,Exploitation,1,1,0,0.0009765625,0.0009765625,0.0 +xiiotid,0.75,2,Tampering,Lateral_movement,222,183,-39,0.216796875,0.1787109375,-0.0380859375 +xiiotid,0.75,2,Tampering,Normal,0,0,0,0.0,0.0,0.0 +xiiotid,0.75,2,Tampering,RDOS,0,0,0,0.0,0.0,0.0 +xiiotid,0.75,2,Tampering,Reconnaissance,0,0,0,0.0,0.0,0.0 +xiiotid,0.75,2,Tampering,Tampering,798,838,40,0.779296875,0.818359375,0.0390625 +xiiotid,0.75,2,Tampering,Weaponization,1,0,-1,0.0009765625,0.0,-0.0009765625 +xiiotid,0.75,2,Weaponization,C&C,0,0,0,0.0,0.0,0.0 +xiiotid,0.75,2,Weaponization,Exfiltration,0,0,0,0.0,0.0,0.0 +xiiotid,0.75,2,Weaponization,Exploitation,207,317,110,0.01606643899410121,0.024604160198696056,0.008537721204594846 +xiiotid,0.75,2,Weaponization,Lateral_movement,0,0,0,0.0,0.0,0.0 +xiiotid,0.75,2,Weaponization,Normal,0,0,0,0.0,0.0,0.0 +xiiotid,0.75,2,Weaponization,RDOS,0,0,0,0.0,0.0,0.0 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+xiiotid,0.25,2,2,Exploitation,227,0.016488046166529265,0.016525977886101533,3.793171957226782e-05,0.6167400881057269,0.6123348017621145,-0.004405286343612369,0.03211745813259922,0.032183375781430885,6.591764883166762e-05 +xiiotid,0.25,2,3,Lateral_movement,6319,0.9145810663764962,0.9127383951061533,-0.0018426712703428771,0.7980693147649944,0.8028169014084507,0.004747586643456314,0.852362038367278,0.8542561252841626,0.0018940869168846408 +xiiotid,0.25,2,4,Normal,83651,0.9710152233088013,0.9709588140883874,-5.640922041394436e-05,0.9211127183177726,0.918470789350994,-0.002641928966778617,0.9454059127746115,0.9439857476348446,-0.001420165139766949 +xiiotid,0.25,2,5,RDOS,28252,0.9919369036301539,0.9920766278127972,0.0001397241826432971,0.9971683420642786,0.9971683420642786,0.0,0.9945457433851694,0.9946159685078289,7.02251226594175e-05 +xiiotid,0.25,2,6,Reconnaissance,25474,0.9916464891041162,0.9916451283284246,-1.360775691683358e-06,0.6430870691685641,0.6476407317264662,0.004553662557902127,0.7802066961946944,0.7835482415520886,0.003341545357394171 +xiiotid,0.25,2,7,Tampering,1024,0.8247096092925026,0.8340471092077087,0.00933749991520616,0.7626953125,0.7607421875,-0.001953125,0.7924911212582446,0.7957099080694586,0.003218786811214014 +xiiotid,0.25,2,8,Weaponization,12884,0.8660329617725501,0.8605920065475379,-0.005440955225012201,0.9829245575908103,0.9793542378143434,-0.0035703197764669348,0.9207838004871487,0.9161402744500109,-0.004643526037137802 +xiiotid,0.5,0,0,C&C,573,0.09224011713030747,0.08453441295546558,-0.007705704174841885,0.9895287958115183,0.9109947643979057,-0.07853403141361259,0.16875,0.15471250740960285,-0.01403749259039716 +xiiotid,0.5,0,1,Exfiltration,4427,1.0,1.0,0.0,0.9516602665461938,0.9602439575333183,0.008583690987124415,0.9752314814814814,0.9797188292233234,0.004487347741842007 +xiiotid,0.5,0,2,Exploitation,227,0.018892228424216402,0.014540647719762063,-0.004351580704454339,0.5814977973568282,0.5814977973568282,0.0,0.03659550873301912,0.028371843095110157,-0.008223665637908967 +xiiotid,0.5,0,3,Lateral_movement,6319,0.9643082091119043,0.9651458782063834,0.0008376690944791321,0.7268555151131508,0.7800284855198607,0.053172970406709874,0.8289117487818085,0.8627691230526868,0.033857374270878315 +xiiotid,0.5,0,4,Normal,83651,0.9706242809264168,0.9705492305767547,-7.505034966215351e-05,0.9278430622467155,0.9273768394878722,-0.00046622275884333764,0.9487516425755586,0.9484720108081011,-0.00027963176745748886 +xiiotid,0.5,0,5,RDOS,28252,0.9924255768892021,0.9925644007470839,0.00013882385788177576,0.9970975506158856,0.9969559677190996,-0.00014158289678600777,0.9947560781821071,0.9947553373713115,-7.408107955964027e-07 +xiiotid,0.5,0,6,Reconnaissance,25474,0.9917997142324657,0.9944206008583691,0.002620886625903429,0.6267174373871398,0.6366883881604773,0.009970950773337539,0.7680835197613721,0.7763258663603292,0.008242346598957062 +xiiotid,0.5,0,7,Tampering,1024,0.5628808864265928,0.7361918604651163,0.17331097403852347,0.9921875,0.9892578125,-0.0029296875,0.7182750088370449,0.8441666666666666,0.12589165782962175 +xiiotid,0.5,0,8,Weaponization,12884,0.8789910158949551,0.9728469779995093,0.09385596210455416,0.9871934181931077,0.9232381248059609,-0.06395529338714678,0.9299553995759303,0.9473935725379315,0.01743817296200123 +xiiotid,0.5,1,0,C&C,573,0.08977834476160101,0.09979016087666123,0.010011816115060218,0.9825479930191972,0.7469458987783595,-0.23560209424083767,0.16452367036820573,0.1760592348827643,0.01153556451455856 +xiiotid,0.5,1,1,Exfiltration,4427,0.9997687326549491,1.0,0.00023126734505085622,0.9765077930878698,0.9552744522249831,-0.02123334086288675,0.9880013712718546,0.9771256931608133,-0.010875678111041287 +xiiotid,0.5,1,2,Exploitation,227,0.0234779792746114,0.031936579841449604,0.008458600566838205,0.6387665198237885,0.6211453744493393,-0.017621145374449254,0.04529126971732001,0.06074967686342094,0.01545840714610093 +xiiotid,0.5,1,3,Lateral_movement,6319,0.9541181736794987,0.9593312245820154,0.005213050902516669,0.6746320620351322,0.6719417629371736,-0.002690299097958593,0.790395846852693,0.7903210795718939,-7.47672807991373e-05 +xiiotid,0.5,1,4,Normal,83651,0.9725,0.9710573675517097,-0.0014426324482903485,0.9161038122676358,0.9417460640040167,0.025642251736380905,0.9434598740543302,0.9561771363721879,0.012717262317857747 +xiiotid,0.5,1,5,RDOS,28252,0.9948775921150246,0.9965285157633723,0.0016509236483477352,0.9968143848223134,0.995752513096418,-0.0010618717258954469,0.9958450467653247,0.9961403633015828,0.0002953165362581389 +xiiotid,0.5,1,6,Reconnaissance,25474,0.99514972585407,0.992357048499717,-0.002792677354353068,0.7409908141634608,0.8257046400251237,0.08471382586166287,0.8494667206696368,0.9013927576601671,0.05192603699053033 +xiiotid,0.5,1,7,Tampering,1024,0.4755278310940499,0.5771428571428572,0.10161502604880729,0.9677734375,0.986328125,0.0185546875,0.6377091377091377,0.7281903388608507,0.09048120115171299 +xiiotid,0.5,1,8,Weaponization,12884,0.9481128563984218,0.9643887623386485,0.016275905940226654,0.9885128841974542,0.9857963365414467,-0.002716547656007484,0.9678914769920584,0.9749750518154603,0.007083574823401939 +xiiotid,0.5,2,0,C&C,573,0.1745398773006135,0.07111611048618922,-0.10342376681442426,0.9930191972076788,0.9930191972076788,0.0,0.29689538220714845,0.13272684861208303,-0.16416853359506542 +xiiotid,0.5,2,1,Exfiltration,4427,0.9997638724911452,0.9997647058823529,8.333912077107541e-07,0.9564038852496047,0.9597921843234696,0.003388299073864842,0.9776033248672362,0.9793707502593062,0.0017674253920699767 +xiiotid,0.5,2,2,Exploitation,227,0.020633750921149593,0.031478102189781025,0.010844351268631432,0.6167400881057269,0.6079295154185022,-0.008810572687224738,0.0399315459212778,0.05985686402081979,0.01992531809954199 +xiiotid,0.5,2,3,Lateral_movement,6319,0.9250299880047981,0.9301369863013699,0.005106998296571796,0.7322361133090679,0.8596296882418104,0.1273935749327425,0.8174189559226216,0.8934945308002303,0.07607557487760874 +xiiotid,0.5,2,4,Normal,83651,0.9704520181344934,0.9706830191140975,0.00023100097960404575,0.9493610357317904,0.8893856618570011,-0.05997537387478935,0.9597906746271543,0.9282577232120577,-0.0315329514150966 +xiiotid,0.5,2,5,RDOS,28252,0.992565972589226,0.994596312778131,0.002030340188905022,0.9971683420642786,0.9967789890981169,-0.00038935296616171566,0.9948618345546041,0.9956864547608104,0.0008246202062063279 +xiiotid,0.5,2,6,Reconnaissance,25474,0.9787918845910685,0.9713854042670085,-0.007406480324059994,0.6685247703540865,0.8328884352673314,0.16436366491324483,0.7944393907587527,0.8968213712063572,0.10238198044760449 +xiiotid,0.5,2,7,Tampering,1024,0.43615344193378874,0.7971602434077079,0.3610068014739191,0.810546875,0.767578125,-0.04296875,0.567133583874274,0.782089552238806,0.214955968364532 +xiiotid,0.5,2,8,Weaponization,12884,0.9035706649561684,0.9876680722412284,0.08409740728505999,0.9840111766532132,0.9635206457621857,-0.020490530891027592,0.9420769087869217,0.975444937728362,0.03336802894144031 +xiiotid,0.75,0,0,C&C,573,0.17078443483631872,0.14609375,-0.024690684836318727,0.9650959860383944,0.9790575916230366,0.013961605584642212,0.2902125426397271,0.25424881033310676,-0.035963732306620366 +xiiotid,0.75,0,1,Exfiltration,4427,0.9995259540175397,0.9997432605905007,0.00021730657296092915,0.9525638129658911,0.8796024395753331,-0.07296137339055797,0.9754799907471663,0.935832732516222,-0.03964725823094428 +xiiotid,0.75,0,2,Exploitation,227,0.019334993408524976,0.019906499773789776,0.0005715063652648004,0.5814977973568282,0.5814977973568282,0.0,0.037425574142330595,0.03849518810148732,0.0010696139591567233 +xiiotid,0.75,0,3,Lateral_movement,6319,0.9320566194837635,0.9318484042553191,-0.0002082152284443861,0.8857414147808198,0.8871656907738567,0.0014242759930368498,0.9083089905874715,0.9089582488852858,0.0006492582978142591 +xiiotid,0.75,0,4,Normal,83651,0.9711247860758111,0.9714045518787465,0.00027976580293542774,0.956473921411579,0.9531147266619646,-0.003359194749614436,0.9637436762225969,0.9621727298508994,-0.0015709463716975236 +xiiotid,0.75,0,5,RDOS,28252,0.9921110093681764,0.9921110093681764,0.0,0.9970975506158856,0.9970975506158856,0.0,0.9945980298697172,0.9945980298697172,0.0 +xiiotid,0.75,0,6,Reconnaissance,25474,0.983384137230291,0.9943473495991233,0.010963212368832242,0.6458742246996938,0.6767292141006517,0.030854989400957877,0.7796706551356475,0.8053537642194762,0.02568310908382876 +xiiotid,0.75,0,7,Tampering,1024,0.800203873598369,0.8677595628415301,0.06755568924316113,0.7666015625,0.775390625,0.0087890625,0.7830423940149626,0.8189788550799381,0.0359364610649755 +xiiotid,0.75,0,8,Weaponization,12884,0.9104435110699793,0.9297005900779486,0.019257079007969247,0.9926265135051227,0.9905308910276311,-0.00209562247749151,0.9497604990531358,0.9591522302807111,0.009391731227575306 +xiiotid,0.75,1,0,C&C,573,0.10998834045860863,0.1218197498921949,0.011831409433586279,0.987783595113438,0.9860383944153578,-0.0017452006980802626,0.19793670222066795,0.21684897332565722,0.018912271104989276 +xiiotid,0.75,1,1,Exfiltration,4427,0.9997648165569144,1.0,0.0002351834430855959,0.9602439575333183,0.9604698441382427,0.00022588660492439683,0.9796059453854131,0.9798363866804932,0.00023044129508009714 +xiiotid,0.75,1,2,Exploitation,227,0.026335040234089245,0.04698205546492659,0.020647015230837347,0.6343612334801763,0.6343612334801763,0.0,0.050570676031606664,0.08748481166464156,0.036914135633034896 +xiiotid,0.75,1,3,Lateral_movement,6319,0.9445538057742782,0.9406710974225968,-0.003882708351681363,0.9112201297673682,0.9183415097325526,0.00712137996518436,0.9275875956504227,0.929372197309417,0.0017846016589943003 +xiiotid,0.75,1,4,Normal,83651,0.9729585703486139,0.9725495513262992,-0.0004090190223147161,0.9385303224109693,0.9432164588588301,0.004686136447860756,0.9554343989972132,0.957658439485614,0.002224040488400858 +xiiotid,0.75,1,5,RDOS,28252,0.9938247644588729,0.9931953601523111,-0.000629404306561776,0.9968851762707065,0.9970975506158856,0.00021237434517906717,0.9953526179074411,0.9951426300450411,-0.00020998786240000733 +xiiotid,0.75,1,6,Reconnaissance,25474,0.9917502787068004,0.9910376901463036,-0.0007125885604968296,0.698437622674099,0.8030540943707309,0.10461647169663191,0.819643432993965,0.8871975019516003,0.06755406895763527 +xiiotid,0.75,1,7,Tampering,1024,0.857,0.880469583778015,0.023469583778015002,0.8369140625,0.8056640625,-0.03125,0.8468379446640316,0.8414074451810301,-0.005430499483001561 +xiiotid,0.75,1,8,Weaponization,12884,0.9187711346140011,0.9342963995013566,0.015525264887355505,0.9911518162061471,0.9889009624340267,-0.0022508537721204203,0.9535899637829967,0.9608234983597904,0.007233534576793765 +xiiotid,0.75,2,0,C&C,573,0.18441127694859039,0.20878274268104777,0.02437146573245738,0.9703315881326352,0.9458987783595113,-0.024432809773123898,0.30992196209587514,0.34206374250552224,0.0321417804096471 +xiiotid,0.75,2,1,Exfiltration,4427,0.9997643732327992,0.9997643732327992,0.0,0.9584368646939236,0.9584368646939236,0.0,0.9786645138968977,0.9786645138968977,0.0 +xiiotid,0.75,2,2,Exploitation,227,0.03358925143953935,0.03121516164994426,-0.00237408978959509,0.6167400881057269,0.6167400881057269,0.0,0.06370875995449374,0.059422750424448216,-0.004286009530045522 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@@ -0,0 +1,7 @@ +dataset,alpha,selected_beta,total,improved,degraded,unchanged,mean_delta,median_delta,min_delta,max_delta +xiiotid,0.25,0.0,30,1,1,28,-0.001517806604221289,0.0,-0.026277262664796908,0.02494573827906854 +xiiotid,0.5,0.75,30,12,1,17,0.03024920134564162,0.012286627106224551,-0.2565947651121153,0.3235930735930736 +xiiotid,0.75,0.5,30,8,2,20,0.01167502563325023,0.010269879504053326,-0.0697526762643042,0.06759906759906764 +5gnidd,0.25,0.0,30,1,0,29,0.002111576617816057,0.0,-0.019933554817275767,0.07142857142857151 +5gnidd,0.5,1.0,30,15,6,9,0.03903400685752253,0.02024848480689545,-0.17567567567567566,0.4040650406504065 +5gnidd,0.75,0.5,30,8,1,21,0.02071412212202052,0.004477101314292853,-0.03633656881678371,0.16839803756061722 diff --git a/artifacts/figures/final/figI_per_client_original_vs_proposed.pdf b/artifacts/figures/final/figI_per_client_original_vs_proposed.pdf new file mode 100644 index 0000000..912ba34 Binary files /dev/null and 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a/artifacts/figures/final/figK_confusion_delta_5gnidd_a0.75_seed0.pdf b/artifacts/figures/final/figK_confusion_delta_5gnidd_a0.75_seed0.pdf new file mode 100644 index 0000000..7c0ee78 Binary files /dev/null and b/artifacts/figures/final/figK_confusion_delta_5gnidd_a0.75_seed0.pdf differ diff --git a/artifacts/figures/final/figK_confusion_delta_5gnidd_a0.75_seed0.png b/artifacts/figures/final/figK_confusion_delta_5gnidd_a0.75_seed0.png new file mode 100644 index 0000000..f90e630 Binary files /dev/null and b/artifacts/figures/final/figK_confusion_delta_5gnidd_a0.75_seed0.png differ diff --git a/docs/research/ORIGINAL_VS_PROPOSED_DEEP_COMPARISON.md b/docs/research/ORIGINAL_VS_PROPOSED_DEEP_COMPARISON.md new file mode 100644 index 0000000..8a4d45e --- /dev/null +++ b/docs/research/ORIGINAL_VS_PROPOSED_DEEP_COMPARISON.md @@ -0,0 +1,359 @@ +# Original PROTEAN vs Proposed Beta-Tuned Aggregation: Deep Paired Comparison + +Status: **descriptive post-hoc analysis of completed experiments**. No training, no partition +regeneration, no modification of any result artifact. Every number is derived from the audited +machine-readable outputs in `results/` and `artifacts/analysis/`, using the reproducible scripts +`scripts/deep_comparison.py` (derived tables) and `scripts/make_comparison_figures.py` +(Figures I–K). + +Primary comparison: + +``` +original_protean (frozen reproduced baseline) + vs +support_weighted(beta*) (validation-selected beta per dataset-alpha condition) +``` + +This document does **not** substitute `support_weighted(beta=0)` for the baseline anywhere; +`beta=0` appears only as an explicitly labelled control. + +--- + +## 1. Research question and comparison scope + +The question is not whether the proposed weighting is universally better. It is: **where, on the +completed grid, does the validation-selected `support_weighted` run measurably differ from +`original_protean`, in which direction, for which clients and classes, and how large are those +differences relative to seed variability?** + +Six dataset-alpha conditions (2 datasets × 3 Dirichlet alpha), three seeds each (18 paired cells, 30 +client-seed observations per condition). All comparisons are on the **test** split, using the same +partition, split, class mapping, and initialization within each cell. + +## 2. Original PROTEAN versus proposed mechanism + +Only the **server-side global prototype aggregation** changes: + +$$ +\bar{C}_j(\beta) = \frac{\sum_{i \in S_j} N_{ij}^{\beta} C_{ij}}{\sum_{i \in S_j} N_{ij}^{\beta}}, +\qquad +w_{ij}(\beta) = \frac{N_{ij}^{\beta}}{\sum_{k \in S_j} N_{kj}^{\beta}} +$$ + +- `original_protean` uses equal contributions over `S_j` (the reference Eq. 2 behavior under the + documented participant rule). +- `support_weighted(beta*)` reweights each contributing client by its class-specific training support + `N_ij`, at the beta selected by pooled validation. + +**Unchanged:** local prototype construction, model aggregation, architecture, loss, and the +nearest-prototype inference rule. **Changed:** only the prototype aggregation weights. +`N_ij` is class-specific sample support, not prototype quality. + +Selected betas (source: `artifacts/analysis/final__selection_strength.csv`), verified: + +| Dataset | alpha=0.25 | alpha=0.50 | alpha=0.75 | +|---|---|---|---| +| X-IIoTID | 0.00 | 0.75 | 0.50 | +| 5G-NIDD | 0.00 | 1.00 | 0.50 | + +## 3. Pairing and evaluation methodology + +- **Pairing verified** for all 18 cells: same dataset, alpha, seed, and **identical + `manifest_content_hash`** between the `original_protean` and `support_weighted` runs + (`scripts/deep_comparison.py` raises on any mismatch). +- `Delta = proposed − original` everywhere. Percentage points (pp) are never conflated with relative + percent. +- Per-client test metrics come from each run's `per_client.csv` (rare-class macro-F1 and recall are + computed on each client's own evaluation partition using that client's own rare-class ids; + all 10 clients are eligible in every condition). Per-class metrics come from `test_per_class.csv`; + confusion matrices from `test_confusion_matrix.json`. +- **Effectively-unchanged band = ±0.02** for paired per-client deltas, chosen to reflect the observed + `original_protean` vs `support_weighted(beta=0)` residual divergence (the two "selected beta = 0" + conditions). Raw deltas are stored in `deepcmp__per_client.csv` so any other threshold can be + applied. +- Three seeds do not support inferential significance testing; all statements are descriptive. + +Derived tables: `deepcmp__global_per_seed.csv`, `deepcmp__global_summary.csv`, +`deepcmp__per_client.csv`, `deepcmp__per_client_by_condition.csv`, `deepcmp__per_class.csv`, +`deepcmp__confusion_delta.csv`, `deepcmp__client_support_join.csv`. + +## 4. Six-condition global metric comparison + +Test metrics, mean over seeds `{0,1,2}`; `Δ = proposed − original` (source: +`deepcmp__global_summary.csv`). + +| Dataset | alpha | beta* | rare-F1 orig | rare-F1 prop | **Δ rare-F1** | Δ rare-recall | Δ macro-F1 | Δ weighted-F1 | Δ accuracy | +|---|---|---|---|---|---|---|---|---|---| +| X-IIoTID | 0.25 | 0.00 | 0.6328 | 0.6321 | **−0.0007** | −0.0007 | −0.0007 | −0.0008 | −0.0014 | +| X-IIoTID | 0.50 | 0.75 | 0.6425 | 0.6655 | **+0.0230** | −0.0144 | +0.0222 | +0.0089 | +0.0069 | +| X-IIoTID | 0.75 | 0.50 | 0.6039 | 0.6052 | **+0.0014** | −0.0077 | +0.0058 | +0.0061 | +0.0075 | +| 5G-NIDD | 0.25 | 0.00 | 0.8423 | 0.8412 | **−0.0010** | +0.0007 | −0.0028 | −0.0010 | −0.0007 | +| 5G-NIDD | 0.50 | 1.00 | 0.8805 | 0.8720 | **−0.0086** | −0.0058 | −0.0038 | −0.0005 | −0.0007 | +| 5G-NIDD | 0.75 | 0.50 | 0.8799 | 0.8967 | **+0.0168** | −0.0015 | +0.0117 | +0.0014 | +0.0013 | + +Per-seed rare-class macro-F1 deltas (seed 0 / 1 / 2), showing the seed spread: + +| Dataset | alpha | beta* | Δ seed 0 | Δ seed 1 | Δ seed 2 | +|---|---|---|---|---|---| +| X-IIoTID | 0.25 | 0.00 | +0.0016 | −0.0020 | −0.0016 | +| X-IIoTID | 0.50 | 0.75 | +0.0266 | +0.0163 | +0.0261 | +| X-IIoTID | 0.75 | 0.50 | −0.0097 | +0.0101 | +0.0036 | +| 5G-NIDD | 0.25 | 0.00 | −0.0050 | −0.0010 | +0.0029 | +| 5G-NIDD | 0.50 | 1.00 | +0.0090 | −0.0811 | +0.0464 | +| 5G-NIDD | 0.75 | 0.50 | −0.0011 | +0.0450 | +0.0067 | + +Interpretation: + +- **Two conditions are numerically null**: both `alpha=0.25` conditions selected `beta=0`, so the + selected run is the equal-weight `support_weighted` run; their small deltas are floating-point + divergence, not a weighting effect. +- Among the **four genuine weighting conditions (beta>0)**, rare-class macro-F1 improved in three + (X-IIoTID a0.50 +0.023, X-IIoTID a0.75 +0.001, 5G-NIDD a0.75 +0.017) and degraded in one + (5G-NIDD a0.50 −0.009). +- 5G-NIDD a0.50 is dominated by **seed variance** (−0.081 at seed 1 versus +0.046 at seed 2); its + pooled mean is not a reliable condition-level statement. +- **Rare-class recall drops in five of six conditions** (and in **all four** beta>0 conditions) + even when rare-class macro-F1 rises: the F1 gains are largely precision-driven (see §7). +- Overall macro-F1 moves **in the same direction** as rare-class F1 in every condition; where rare-F1 + improves, overall macro-F1 also improves (X-IIoTID a0.50 +0.022, 5G-NIDD a0.75 +0.012). There is + no condition with a rare-class gain accompanied by an overall-metric collapse. + +## 5. Rare-class performance trade-offs + +The recurring pattern is a **precision–recall trade-off**, not a uniform improvement: + +- X-IIoTID a0.50: rare-F1 +0.0230 while rare-recall −0.0144 → rare-class precision rose. +- X-IIoTID a0.75: rare-F1 +0.0014 while rare-recall −0.0077. +- 5G-NIDD a0.75: rare-F1 +0.0168 while rare-recall −0.0015. +- 5G-NIDD a0.50: rare-F1 −0.0086 and rare-recall −0.0058 (both down; not a trade-off but a + regression, and seed-dominated). + +So the weighting can raise the rare-class **balance** metric by suppressing false positives on rare +classes while giving up a little rare-class coverage. + +## 6. Paired per-client results (test, vs original_protean) + +Figure I: `artifacts/figures/final/figI_per_client_original_vs_proposed.png`. +Per-observation table: `deepcmp__per_client.csv` (30 observations per condition). + +| Condition | beta* | improved | degraded | unchanged | mean Δ | min Δ | max Δ | +|---|---|---|---|---|---|---|---| +| X-IIoTID a0.25 | 0.00 | 1 | 1 | 28 | −0.0015 | −0.026 | +0.025 | +| X-IIoTID a0.50 | 0.75 | 12 | 1 | 17 | +0.0302 | −0.257 | +0.324 | +| X-IIoTID a0.75 | 0.50 | 8 | 2 | 20 | +0.0117 | −0.070 | +0.068 | +| 5G-NIDD a0.25 | 0.00 | 1 | 0 | 29 | +0.0021 | −0.020 | +0.071 | +| 5G-NIDD a0.50 | 1.00 | 15 | 6 | 9 | +0.0390 | −0.176 | +0.404 | +| 5G-NIDD a0.75 | 0.50 | 8 | 1 | 21 | +0.0207 | −0.036 | +0.168 | + +Counts use the ±0.02 effectively-unchanged band on per-client test rare-class macro-F1. + +- At the two `beta=0` conditions the counts collapse to essentially "unchanged" (28/30 and 29/30), + confirming that the beta=0 run is numerically the equal-weight control. +- At beta>0 conditions the response is **heterogeneous**: X-IIoTID a0.50 improves a majority + (12/30) with only 1 clear degradation but contains the single largest loss (−0.257); 5G-NIDD a0.50 + improves 15/30 but degrades 6/30. +- Client deltas are not small in the tails: individual clients move by ±0.17–0.40 at the strongest + conditions. A condition-level mean therefore hides material client-level redistribution. +- Client ids are per-seed realizations; id `k` is not the same entity across seeds and is never + pooled. + +## 7. Per-class precision/recall/F1 changes (beta>0 conditions) + +Figure J: `artifacts/figures/final/figJ_per_class_original_vs_proposed.png`. +Source: `deepcmp__per_class.csv` (per seed) aggregated as the mean delta over the three seeds; class +support is the mean test support over seeds. X-IIoTID and 5G-NIDD labels are never mixed. + +X-IIoTID, alpha=0.50 (beta=0.75): + +| Class | Δ F1 | Δ recall | Δ precision | support~ | +|---|---|---|---|---| +| Tampering | **+0.144** | −0.009 | +0.212 | 1,024 | +| Reconnaissance | +0.054 | +0.086 | −0.003 | 25,474 | +| Lateral_movement | +0.037 | +0.059 | +0.004 | 6,319 | +| Weaponization | +0.019 | −0.029 | +0.065 | 12,884 | +| Normal | −0.006 | −0.012 | −0.000 | 83,651 | +| C&C | **−0.056** | −0.105 | −0.034 | 573 | + +X-IIoTID, alpha=0.75 (beta=0.50): largest gains Reconnaissance (+0.033) and Tampering (+0.010); +largest loss Exfiltration (−0.013). + +5G-NIDD, alpha=0.50 (beta=1.00): largest gain TCPConnectScan (+0.034, precision-driven); largest +losses SYNFlood (−0.043, precision −0.073) and SlowrateDoS (−0.013). 5G-NIDD, alpha=0.75 (beta=0.50): +largest gain ICMPFlood (+0.097, precision +0.102 on a very small support of ~231); losses SYNFlood +(−0.009). + +Points to note: + +- Gains are **not confined to rare classes**: at X-IIoTID a0.50 the largest gain is on the + low-support class **Tampering**, but a large gain also appears on the higher-support class + **Reconnaissance** (recall-driven). +- The largest per-class **degradations** are on low-support attack classes with volatile F1: + **C&C** at X-IIoTID a0.50 and **SYNFlood** at 5G-NIDD a0.50. Both are precision losses at very + small support (~573 and ~1,944). +- **Benign detection is essentially unchanged** (Δ F1 +0.0009 to +0.0019; recall within ±0.002), + so the weighting does not trade benign detection for rare-class gains. +- Increased rare-class recall does **not** systematically accompany increased false positives: + X-IIoTID Reconnaissance recall rose +0.086 with precision −0.003, i.e. almost free recall. + Conversely, Tampering F1 rose via precision, not recall. + +## 8. Confusion-matrix difference analysis + +Figure K shows the paired difference for **5G-NIDD, alpha=0.75, seed=0** (the same pair as Figure H): +`artifacts/figures/final/figK_confusion_delta_5gnidd_a0.75_seed0.png`. Rows = true class, columns = +predicted class; both matrices have identical class order and 243,177 test samples. Source: +`deepcmp__confusion_delta.csv`. + +Largest raw-count changes for this pair (proposed − original): + +| Change | true → predicted | original | proposed | Δ count | Δ row-rate | +|---|---|---|---|---|---| +| Reduced error | HTTPFlood → SlowrateDoS | 759 | 212 | **−547** | −1.94 pp | +| Reduced error | Benign → TCPConnectScan | 2,015 | 1,495 | **−520** | −0.54 pp | +| Increased error | SlowrateDoS → HTTPFlood | 1,079 | 1,308 | **+229** | +1.57 pp | +| Increased error | Benign → UDPFlood | 56,899 | 57,054 | **+155** | +0.16 pp | +| Diagonal | HTTPFlood → HTTPFlood | 27,399 | 27,946 | +547 | +1.94 pp | +| Diagonal | Benign → Benign | 36,263 | 36,551 | +288 | +0.30 pp | +| Diagonal | SlowrateDoS → SlowrateDoS | 13,480 | 13,241 | −239 | −1.63 pp | + +- The dominant residual error, **Benign → UDPFlood (~57k samples)**, is essentially unchanged; the + proposed weighting neither fixes nor meaningfully worsens it. The +155 cell must not be described + as an improvement. +- The main benefit here is a **net reduction of the HTTPFlood ↔ SlowrateDoS confusion**: the + HTTPFlood → SlowrateDoS cell lost 547 samples while the reverse gained 229, and HTTPFlood diagonal + gained 547 — consistent with higher HTTPFlood precision. +- The main cost here is a **small loss of SlowrateDoS diagonal** (−239) with a +229 shift into + SlowrateDoS → HTTPFlood. +- Across all four beta>0 conditions (summed raw counts), the largest **reduced** misclassifications + are X-IIoTID `Reconnaissance → Exploitation` (−6,773), `Reconnaissance → Weaponization` (−3,398), + `Lateral_movement → Tampering` (−1,699) and 5G-NIDD `Benign → TCPConnectScan` (−1,484); the largest + **increased** ones are X-IIoTID `Normal → C&C` (+2,546) and `Weaponization → Exploitation` (+1,286). + Raw counts are support-sensitive, so these are descriptive; the row-normalized view (Figure K, + right panel) is the support-fair comparison. + +## 9. Support imbalance versus client outcomes + +Join: per-client test delta (beta>0 conditions only) against the client's training support from +`rare_unseen.csv` (`deepcmp__client_support_join.csv`, n = 120 client-seed observations). + +| Statistic | Value | +|---|---| +| Pearson(Δ rare-F1, rare-class support fraction) | −0.092 | +| Pearson(Δ rare-F1, total client samples) | −0.009 | +| Low rare-support quartile (≤72 rare-class samples) mean Δ | **+0.041** | +| High rare-support quartile (≥290) mean Δ | **+0.008** | +| Mean Δ for clients with 0 missing classes (n=101) | +0.022 | +| Mean Δ for clients with exactly 1 missing class (n=16) | +0.045 | + +- There is **no evidence that low-support clients are disproportionately harmed**; the linear + association is near zero and the low-support quartile improved, on average, at least as much as the + high-support quartile. +- Clients with one missing class improved more on average, but n is small. +- **Limitation:** this is a descriptive association across 120 noisy observations, not a causal test; + the beta>0 conditions confound `beta`, dataset, and alpha, and no significance test is valid with + three seeds. + +## 10. Prototype displacement and interpretation + +From `final__prototype_displacement_by_beta.csv` (fixed round-0 snapshots, identical local prototypes +and `N_ij`, no retraining), mean `‖Cbar_j(1) − Cbar_j(0)‖`: + +| Dataset | alpha=0.25 | alpha=0.50 | alpha=0.75 | +|---|---|---|---| +| X-IIoTID | 3.422 | 2.099 | 1.305 | +| 5G-NIDD | 2.919 | 2.340 | 1.385 | + +- Displacement is monotone in beta and largest where realized support imbalance is largest. +- The observed per-class/per-client detection changes are **consistent with** the mechanism chain + support imbalance → weight redistribution → prototype displacement → detection change, but the + experiment **does not isolate** the displacement→detection step (training is end-to-end). + Fixed-snapshot displacement must not be read as detection quality. + +## 11. Conditions where the proposed method helps + +- **X-IIoTID α=0.50 (beta=0.75)**: the clearest case. Rare-class macro-F1 +0.0230, overall macro-F1 + +0.0222, accuracy +0.0069; 12/30 client observations improved (1 degraded). Gains driven by + Reconnaissance recall and Tampering precision; rare-class recall traded down slightly. +- **5G-NIDD α=0.75 (beta=0.50)**: rare-class macro-F1 +0.0168, macro-F1 +0.0117; 8/30 improved, + 1 degraded. +- **X-IIoTID α=0.75 (beta=0.50)**: marginal rare-F1 gain (+0.0014) with broader macro-F1/accuracy + gains (+0.0058/+0.0075). + +## 12. Conditions where the proposed method hurts + +- **5G-NIDD α=0.50 (beta=1.00)**: rare-class macro-F1 −0.0086 and macro-F1 −0.0038, seed-dominated + (−0.081 at seed 1). 6/30 client observations degraded. The clearest case against stronger weighting. +- **Both α=0.25 conditions**: the selected beta is 0 (equal weighting), so there is no weighting + benefit; the small deltas are numerical. At `beta=1` these conditions show the largest per-client + degradations, which is why validation did not select a nonzero beta there. +- **Specific classes**: X-IIoTID **C&C** (−0.056 F1) and 5G-NIDD **SYNFlood** (−0.043 F1) degrade at + the strongest `beta`, both low-support classes, and the 5G-NIDD α=0.75 seed-0 confusion shows a + small SlowrateDoS recall loss. +- **No condition** shows a large, consistent, cross-seed rare-class improvement; the largest positive + condition mean is +0.023 and its strongest seed-rate change is within the seed spread. + +## 13. Effect sizes and practical relevance + +- Genuine rare-class macro-F1 effects are **≤ ~0.023** in magnitude, i.e. **≤ 2.3 percentage + points**, and the largest is roughly the size of the between-seed standard deviation at that + condition. They are small relative to seed noise. +- The clearest overall-metric movement is X-IIoTID α=0.50 (macro-F1 +0.022, accuracy +0.007); the + other positive conditions move by ≤ ~0.012 macro-F1. +- Client-level redistribution is larger than the condition-level effect: individual clients move by + up to −0.26 / +0.32 (X-IIoTID α=0.50) even though the mean is +0.03. +- Practically, the weighting is **not a reliable blanket improvement**; it is a small, condition- and + client-dependent adjustment whose sign is not predictable from alpha alone. + +## 14. Threats to validity + +- **Only three seeds**; no inferential test is valid. Condition means are dominated by seed variance + at 5G-NIDD α=0.50. +- **Two `beta=0` conditions are numerical, not weighting effects**; they are retained for + completeness and labelled. +- **`S_j` is a reproduction assumption** (no official PROTEAN code). All comparisons are conditional + on both modes sharing it. +- **Rare-class composition differs by dataset**; absolute cross-dataset levels are not comparable. +- **Per-class metrics on tiny supports** (C&C ~573, ICMPFlood ~231, SYNFlood ~1,944) are volatile. +- **Confusion-matrix raw counts** are support-sensitive; use row-normalized rates for support-fair + comparison. +- Client ids are per-seed realizations and are not pooled across seeds. +- `original_protean` is never substituted by `beta=0`. + +## 15. Evidence-backed scientific conclusions + +1. Over the six conditions, the validation-selected beta produces **small, mixed-sign** test + differences from `original_protean`; it is not a universal improvement. +2. Among the four genuine `beta>0` conditions, rare-class macro-F1 improved in **three** and degraded + in **one**; the largest gain is ~2.3 pp and is within seed variability. +3. When rare-class macro-F1 rises, **overall macro-F1 rises with it**, but **rare-class recall + usually falls** — the gains are precision-driven. +4. Benefits and harms are **client-specific and class-specific**; within a condition some clients + gain and others lose, with individual changes larger than the condition mean. +5. The dominant classification error (Benign → UDPFlood) is **unchanged** by the weighting. The + measurable benefit at the analysed pair is a reduced HTTPFlood ↔ SlowrateDoS confusion. +6. Low-support clients are **not** systematically disadvantaged in the observed data, but the + evidence is descriptive only. +7. Where weighting does not help (both α=0.25 conditions), validation correctly selects `beta=0`. + +**Final question — what measurable benefit does beta-controlled prototype aggregation provide over +original PROTEAN, under which conditions, and at what cost?** +The evidence supports a **modest, condition-dependent benefit**: at X-IIoTID α=0.50 a rare-class +macro-F1 gain of about **+0.023** with a matching overall macro-F1 gain, and smaller gains at X-IIoTID +α=0.75 and 5G-NIDD α=0.75. The cost is a small **rare-class recall reduction** (precision–recall +trade-off), some **client- and class-level regressions** (e.g. X-IIoTID C&C, 5G-NIDD SYNFlood), and a +clear **regression at 5G-NIDD α=0.50**. At α=0.25 the best tested beta is 0 (no benefit). The +evidence does **not** support a consistent advantage, and no claim of statistical superiority is +made. + +--- + +### Source map + +| Claim group | Source | +|---|---| +| Pairing, global/per-client/per-class/confusion deltas | `scripts/deep_comparison.py` → `artifacts/analysis/deepcmp__*.csv` | +| Selected betas | `artifacts/analysis/final__selection_strength.csv` | +| Global test means/stds | `artifacts/analysis/final__six_condition_master.csv` | +| Selected-vs-original descriptive deltas | `artifacts/analysis/final__selected_vs_original.csv` | +| Prototype displacement | `artifacts/analysis/final__prototype_displacement_by_beta.csv` | +| Per-client / per-class / confusion raw | `results//per_client.csv`, `test_per_class.csv`, `test_confusion_matrix.json` | +| Figures I, J, K | `scripts/make_comparison_figures.py` → `artifacts/figures/final/figI/figJ/figK*.{png,pdf}` | diff --git a/scripts/deep_comparison.py b/scripts/deep_comparison.py new file mode 100644 index 0000000..3f8d955 --- /dev/null +++ b/scripts/deep_comparison.py @@ -0,0 +1,239 @@ +#!/usr/bin/env python3 +"""Deep paired comparison: original_protean vs support_weighted(selected beta). + +Reads only completed experimental artifacts under ``results/`` and +``artifacts/analysis/`` and writes derived comparison tables under +``artifacts/analysis/``. No training, no result mutation. + +For every (dataset, alpha, seed) the ``original_protean`` run and the +``support_weighted`` run at the validation-selected beta are matched by +dataset, alpha, seed, and manifest content hash, and compared on test +metrics (global, per-client, per-class, confusion matrices). + +Run: + .venv/bin/python scripts/deep_comparison.py +""" + +from __future__ import annotations + +import csv +import json +from pathlib import Path + +ROOT = Path(__file__).resolve().parents[1] +RESULTS = ROOT / "results" +ANALYSIS = ROOT / "artifacts" / "analysis" + +DATASETS = ["xiiotid", "5gnidd"] +ALPHAS = [0.25, 0.50, 0.75] +SEEDS = [0, 1, 2] +# Validation-selected beta per condition (source: final__selection_strength.csv). +SELECTED = {("xiiotid", 0.25): 0.0, ("xiiotid", 0.5): 0.75, ("xiiotid", 0.75): 0.5, + ("5gnidd", 0.25): 0.0, ("5gnidd", 0.5): 1.0, ("5gnidd", 0.75): 0.5} +GLOBAL_METRICS = ["rare_class_macro_f1", "rare_class_recall", "macro_f1", + "weighted_f1", "accuracy"] +# "Effectively unchanged" band for paired per-client deltas. Chosen to reflect +# the observed original_protean vs support_weighted(beta=0) residual divergence +# (the beta=0 conditions are only semantically equivalent to the original under +# the shared S_j rule, so their per-client residuals are numerical, not a +# weighting effect). Reported alongside the raw deltas so a reader can apply a +# different threshold. +TOL = 0.02 + + +def run_dir(ds: str, alpha: float, seed: int, mode: str, beta: float | None = None) -> Path: + if mode == "original_protean": + return RESULTS / f"{ds}__alpha{alpha:g}__seed{seed}__original_protean" + return RESULTS / f"{ds}__alpha{alpha:g}__seed{seed}__support_weighted__beta{beta:g}" + + +def read_json(p: Path): + return json.loads(p.read_text()) + + +def read_csv(p: Path) -> list[dict]: + with p.open() as fh: + return list(csv.DictReader(fh)) + + +def verify_pairing(ds, alpha, seed, beta) -> tuple[bool, dict]: + o = read_json(run_dir(ds, alpha, seed, "original_protean") / "metadata.json") + s = read_json(run_dir(ds, alpha, seed, "support_weighted", beta) / "metadata.json") + ok = (o["dataset"] == s["dataset"] == ds + and abs(float(o["alpha"]) - alpha) < 1e-9 + and abs(float(s["alpha"]) - alpha) < 1e-9 + and int(o["seed"]) == int(s["seed"]) == seed + and o["manifest_content_hash"] == s["manifest_content_hash"] + and abs(float(s["beta"]) - beta) < 1e-9) + return ok, o + + +def main() -> None: + ANALYSIS.mkdir(parents=True, exist_ok=True) + global_rows, client_rows, class_rows, cm_rows = [], [], [], [] + conditions = [] + + for ds in DATASETS: + for alpha in ALPHAS: + beta = SELECTED[(ds, alpha)] + cond = {"dataset": ds, "alpha": alpha, "selected_beta": beta} + conditions.append(cond) + for seed in SEEDS: + ok, meta = verify_pairing(ds, alpha, seed, beta) + if not ok: + raise SystemExit(f"pairing failed for {ds} a{alpha} s{seed}") + odir = run_dir(ds, alpha, seed, "original_protean") + sdir = run_dir(ds, alpha, seed, "support_weighted", beta) + + og = read_json(odir / "test_global_metrics.json") + sg = read_json(sdir / "test_global_metrics.json") + for m in GLOBAL_METRICS: + global_rows.append({ + "dataset": ds, "alpha": alpha, "seed": seed, + "selected_beta": beta, "metric": m, + "original": og[m], "proposed": sg[m], + "delta": sg[m] - og[m]}) + + opc = {int(r["client_id"]): r for r in read_csv(odir / "per_client.csv")} + spc = {int(r["client_id"]): r for r in read_csv(sdir / "per_client.csv")} + assert set(opc) == set(spc), "client id mismatch" + for cid in sorted(opc): + o, s = opc[cid], spc[cid] + dr = float(s["rare_class_macro_f1"]) - float(o["rare_class_macro_f1"]) + dm = float(s["macro_f1"]) - float(o["macro_f1"]) + da = float(s["accuracy"]) - float(o["accuracy"]) + dre = float(s["rare_class_recall"]) - float(o["rare_class_recall"]) + status = ("unchanged" if abs(dr) <= TOL + else ("improved" if dr > 0 else "degraded")) + client_rows.append({ + "dataset": ds, "alpha": alpha, "seed": seed, + "selected_beta": beta, "client_id": cid, + "n_samples": int(o["n_samples"]), + "orig_rare_f1": o["rare_class_macro_f1"], + "prop_rare_f1": s["rare_class_macro_f1"], + "delta_rare_f1": dr, + "orig_rare_recall": o["rare_class_recall"], + "prop_rare_recall": s["rare_class_recall"], + "delta_rare_recall": dre, + "orig_macro_f1": o["macro_f1"], "prop_macro_f1": s["macro_f1"], + "delta_macro_f1": dm, + "orig_accuracy": o["accuracy"], "prop_accuracy": s["accuracy"], + "delta_accuracy": da, + "status": status}) + + opk = {int(r["class_id"]): r for r in read_csv(odir / "test_per_class.csv")} + spk = {int(r["class_id"]): r for r in read_csv(sdir / "test_per_class.csv")} + for cl in sorted(opk): + o, s = opk[cl], spk[cl] + class_rows.append({ + "dataset": ds, "alpha": alpha, "seed": seed, + "class_id": cl, "class_name": o["class_name"], + "support": int(o["support"]), + "orig_precision": o["precision"], "prop_precision": s["precision"], + "delta_precision": float(s["precision"]) - float(o["precision"]), + "orig_recall": o["recall"], "prop_recall": s["recall"], + "delta_recall": float(s["recall"]) - float(o["recall"]), + "orig_f1": o["f1"], "prop_f1": s["f1"], + "delta_f1": float(s["f1"]) - float(o["f1"])}) + + ocm = read_json(odir / "test_confusion_matrix.json") + scm = read_json(sdir / "test_confusion_matrix.json") + names = meta["class_names"] + for i, t in enumerate(names): + row_o = sum(ocm[i]) + row_s = sum(scm[i]) + for j, p in enumerate(names): + cm_rows.append({ + "dataset": ds, "alpha": alpha, "seed": seed, + "true_class": t, "pred_class": p, + "orig_count": ocm[i][j], "prop_count": scm[i][j], + "delta": scm[i][j] - ocm[i][j], + "orig_row_rate": (ocm[i][j] / row_o) if row_o else 0.0, + "prop_row_rate": (scm[i][j] / row_s) if row_s else 0.0, + "delta_row_rate": ((scm[i][j] / row_s) - (ocm[i][j] / row_o)) + if row_o and row_s else 0.0}) + + _write(ANALYSIS / "deepcmp__global_per_seed.csv", global_rows) + _write(ANALYSIS / "deepcmp__per_client.csv", client_rows) + _write(ANALYSIS / "deepcmp__per_class.csv", class_rows) + _write(ANALYSIS / "deepcmp__confusion_delta.csv", cm_rows) + + # Summaries ------------------------------------------------------------- + summary = [] + for c in conditions: + ds, alpha, beta = c["dataset"], c["alpha"], c["selected_beta"] + for m in GLOBAL_METRICS: + rows = [r for r in global_rows + if r["dataset"] == ds and r["alpha"] == alpha and r["metric"] == m] + orig = [float(r["original"]) for r in rows] + prop = [float(r["proposed"]) for r in rows] + dlt = [float(r["delta"]) for r in rows] + summary.append({ + "dataset": ds, "alpha": alpha, "selected_beta": beta, "metric": m, + "orig_mean": _mean(orig), "orig_std": _std(orig), + "prop_mean": _mean(prop), "prop_std": _std(prop), + "delta_mean": _mean(dlt), "delta_std": _std(dlt), + "per_seed_deltas": "|".join(f"{d:.6f}" for d in dlt), "n": len(rows)}) + _write(ANALYSIS / "deepcmp__global_summary.csv", summary) + + for c in conditions: + ds, alpha = c["dataset"], c["alpha"] + rows = [r for r in client_rows if r["dataset"] == ds and r["alpha"] == alpha] + dlt = [float(r["delta_rare_f1"]) for r in rows] + st = [r["status"] for r in rows] + c["improved"] = st.count("improved") + c["degraded"] = st.count("degraded") + c["unchanged"] = st.count("unchanged") + c["total"] = len(rows) + c["mean_delta"] = _mean(dlt) + c["median_delta"] = _median(dlt) + c["min_delta"] = min(dlt) + c["max_delta"] = max(dlt) + _write(ANALYSIS / "deepcmp__per_client_by_condition.csv", [ + {k: c[k] for k in ("dataset", "alpha", "selected_beta", "total", + "improved", "degraded", "unchanged", + "mean_delta", "median_delta", "min_delta", "max_delta")} + for c in conditions]) + + print("=== GLOBAL SUMMARY (delta = proposed - original) ===") + for r in summary: + print(f" {r['dataset']:8} a{r['alpha']:<4} beta={r['selected_beta']:<4} " + f"{r['metric']:20} orig={float(r['orig_mean']):.4f} " + f"prop={float(r['prop_mean']):.4f} delta={float(r['delta_mean']):+.4f} " + f"per-seed=[{r['per_seed_deltas']}]") + print("=== PER-CLIENT COUNTS (rare-class F1) ===") + for c in conditions: + print(f" {c['dataset']:8} a{c['alpha']:<4} beta={c['selected_beta']:<4} " + f"improved={c['improved']} degraded={c['degraded']} " + f"unchanged={c['unchanged']} (of {c['total']}) " + f"meanDelta={c['mean_delta']:+.4f} min={c['min_delta']:+.4f} " + f"max={c['max_delta']:+.4f}") + print("wrote deepcmp__* derived tables to", ANALYSIS) + + +def _mean(v): + return sum(v) / len(v) if v else 0.0 + + +def _std(v): + if len(v) < 2: + return 0.0 + m = _mean(v) + return (sum((x - m) ** 2 for x in v) / (len(v) - 1)) ** 0.5 + + +def _median(v): + s = sorted(v) + n = len(s) + return s[n // 2] if n % 2 else (s[n // 2 - 1] + s[n // 2]) / 2 + + +def _write(path: Path, rows: list[dict]) -> None: + with path.open("w", newline="") as fh: + w = csv.DictWriter(fh, fieldnames=list(rows[0].keys())) + w.writeheader() + w.writerows(rows) + + +if __name__ == "__main__": + main() diff --git a/scripts/make_comparison_figures.py b/scripts/make_comparison_figures.py new file mode 100644 index 0000000..ac46800 --- /dev/null +++ b/scripts/make_comparison_figures.py @@ -0,0 +1,229 @@ +#!/usr/bin/env python3 +"""Figures I, J, K: original_protean vs support_weighted(selected beta). + +Figure I paired per-client rare-class F1 deltas (test), vs original_protean. +Figure J per-class test F1 change, beta>0 conditions. +Figure K test confusion-matrix difference (5G-NIDD, alpha=0.75, seed=0). + +Reads derived tables written by ``scripts/deep_comparison.py`` and the raw +per-run confusion matrices. Writes PNG (220 dpi) and PDF to +``artifacts/figures/final``. No training, no result mutation. + +Run: + .venv/bin/python scripts/make_comparison_figures.py +""" + +from __future__ import annotations + +import csv +import json +from collections import defaultdict +from pathlib import Path +import statistics + +import matplotlib + +matplotlib.use("Agg") +import matplotlib.pyplot as plt +import numpy as np +from matplotlib.colors import TwoSlopeNorm + +ROOT = Path(__file__).resolve().parents[1] +ANALYSIS = ROOT / "artifacts" / "analysis" +RESULTS = ROOT / "results" +OUT = ROOT / "artifacts" / "figures" / "final" + +CHARCOAL = "#3A3A3A" +TEAL = "#0E7C7B" +RED = "#C0392B" +GREY = "#9A9A9A" +DATASETS = ["xiiotid", "5gnidd"] +DATASET_NAME = {"xiiotid": "X-IIoTID", "5gnidd": "5G-NIDD"} +ALPHAS = [0.25, 0.50, 0.75] +TOL = 0.02 +SEED_MARKERS = {0: "o", 1: "s", 2: "^"} + + +def _style(): + plt.rcParams.update({ + "figure.facecolor": "white", "axes.facecolor": "white", + "axes.edgecolor": "#5A5A5A", "axes.linewidth": 0.9, + "axes.titlesize": 11.5, "axes.titleweight": "bold", + "axes.labelsize": 10.5, "xtick.labelsize": 9, + "ytick.labelsize": 9, "legend.fontsize": 9, + "font.size": 10, "axes.spines.top": False, "axes.spines.right": False, + "grid.color": "#B0B0B0", "grid.alpha": 0.3, "grid.linestyle": ":", + "savefig.dpi": 220, "savefig.bbox": "tight", + }) + + +def _save(fig, name): + OUT.mkdir(parents=True, exist_ok=True) + for ext in ("png", "pdf"): + fig.savefig(OUT / f"{name}.{ext}") + plt.close(fig) + print("wrote", OUT / f"{name}.png") + + +def _read(path): + with path.open() as fh: + return list(csv.DictReader(fh)) + + +# --------------------------------------------------------------------------- +def figure_i(): + rows = _read(ANALYSIS / "deepcmp__per_client.csv") + fig, axes = plt.subplots(2, 3, figsize=(14.0, 7.4), constrained_layout=True, + sharey=True) + ylim = 0.47 + for ri, ds in enumerate(DATASETS): + for ci, alpha in enumerate(ALPHAS): + ax = axes[ri][ci] + ax.set_axisbelow(True) + ax.grid(axis="y") + for seed in (0, 1, 2): + sub = [r for r in rows if r["dataset"] == ds + and abs(float(r["alpha"]) - alpha) < 1e-9 + and int(r["seed"]) == seed] + xs = [int(r["client_id"]) for r in sub] + ys = [float(r["delta_rare_f1"]) for r in sub] + cols = [TEAL if y > TOL else (RED if y < -TOL else GREY) for y in ys] + ax.scatter(xs, ys, s=48, marker=SEED_MARKERS[seed], c=cols, + edgecolor="#333333", linewidth=0.5, alpha=0.9, zorder=4) + ax.axhspan(-TOL, TOL, color="#DDDDDD", alpha=0.35, zorder=1) + ax.axhline(0.0, color="#555555", lw=1.0, zorder=2) + beta = {"xiiotid": {0.25: 0.0, 0.5: 0.75, 0.75: 0.5}, + "5gnidd": {0.25: 0.0, 0.5: 1.0, 0.75: 0.5}}[ds][alpha] + deltas = [float(r["delta_rare_f1"]) for r in rows + if r["dataset"] == ds and abs(float(r["alpha"]) - alpha) < 1e-9] + ax.set_ylim(-ylim, ylim) + ax.set_xlim(-0.7, 9.7) + ax.set_xticks(range(10)) + ax.set_title(f"{DATASET_NAME[ds]} \u03b1={alpha:.2f} " + f"(beta={beta:g})") + ax.text(0.98, 0.04, f"mean \u0394={statistics.mean(deltas):+.3f}", + transform=ax.transAxes, ha="right", va="bottom", + fontsize=8.5, color="#333333") + if ri == 1: + ax.set_xlabel("client id (per-seed partition)") + if ci == 0: + ax.set_ylabel("test rare-class macro-F1\n\u0394 (proposed \u2212 original)") + handles = [plt.Line2D([0], [0], marker="o", ls="", ms=7, mfc=TEAL, mec="#333", label="improved (> +0.02)"), + plt.Line2D([0], [0], marker="o", ls="", ms=7, mfc=RED, mec="#333", label="degraded (< -0.02)"), + plt.Line2D([0], [0], marker="o", ls="", ms=7, mfc=GREY, mec="#333", label="unchanged (|\u0394| \u2264 0.02)"), + plt.Line2D([0], [0], marker="o", ls="", ms=7, mfc="white", mec="#333", label="seed 0"), + plt.Line2D([0], [0], marker="s", ls="", ms=7, mfc="white", mec="#333", label="seed 1"), + plt.Line2D([0], [0], marker="^", ls="", ms=7, mfc="white", mec="#333", label="seed 2")] + fig.legend(handles=handles, loc="upper center", bbox_to_anchor=(0.5, 1.05), + ncols=6, frameon=True, edgecolor="#CCCCCC") + fig.suptitle("Paired per-client test rare-class macro-F1: proposed (selected beta) vs ORIGINAL PROTEAN", + fontsize=12.5, weight="bold", y=1.11) + fig.text(0.5, -0.03, + "Each point is one (client, seed) test observation; x = client id, marker = seed. Points compare the " + "support_weighted run at the validation-selected beta against original_protean on the same partition. " + "At \u03b1=0.25 the selected beta is 0, so the residual spread is numerical (equal-weight equivalence), not a weighting effect. " + "Do not treat client id across seeds as the same real-world client.", + ha="center", va="top", fontsize=8.4, color="#444444", linespacing=1.5) + _save(fig, "figI_per_client_original_vs_proposed") + + +# --------------------------------------------------------------------------- +def figure_j(): + rows = _read(ANALYSIS / "deepcmp__per_class.csv") + fig, axes = plt.subplots(2, 2, figsize=(13.4, 8.2), constrained_layout=True) + for ri, ds in enumerate(DATASETS): + for ci, alpha in enumerate([0.5, 0.75]): + ax = axes[ri][ci] + ax.set_axisbelow(True) + ax.grid(axis="x") + stats = defaultdict(lambda: {"f1": [], "sup": []}) + for r in rows: + if r["dataset"] == ds and abs(float(r["alpha"]) - alpha) < 1e-9: + stats[r["class_name"]]["f1"].append(float(r["delta_f1"])) + stats[r["class_name"]]["sup"].append(int(r["support"])) + names = sorted(stats, key=lambda n: statistics.mean(stats[n]["f1"])) + ys = np.arange(len(names)) + vals = [statistics.mean(stats[n]["f1"]) for n in names] + sup = [int(statistics.mean(stats[n]["sup"])) for n in names] + cols = [TEAL if v >= 0 else RED for v in vals] + ax.barh(ys, vals, color=cols, edgecolor="#333333", linewidth=0.6, zorder=3) + ax.axvline(0.0, color="#555555", lw=1.0, zorder=2) + ax.set_yticks(ys) + ax.set_yticklabels([f"{n} (n\u2248{s:,})" for n, s in zip(names, sup)], + fontsize=8.2) + beta = {"xiiotid": {0.5: 0.75, 0.75: 0.5}, + "5gnidd": {0.5: 1.0, 0.75: 0.5}}[ds][alpha] + ax.set_title(f"{DATASET_NAME[ds]} \u03b1={alpha:.2f} beta={beta:g}") + ax.set_xlabel("\u0394 F1 (proposed \u2212 original), test") + fig.suptitle("Per-class test F1 change for beta>0 conditions (mean over seeds {0,1,2})", + fontsize=12.5, weight="bold", y=1.04) + fig.text(0.5, -0.015, + "Only beta>0 conditions are shown; \u03b1=0.25 selected beta=0 so it carries no weighting effect. " + "Class support n\u2248 is the mean test support over seeds. Teal = F1 increased, red = F1 decreased. " + "X-IIoTID and 5G-NIDD class labels are never mixed. No significance testing (3 seeds).", + ha="center", va="top", fontsize=8.4, color="#444444", linespacing=1.5) + _save(fig, "figJ_per_class_original_vs_proposed") + + +# --------------------------------------------------------------------------- +def figure_k(dataset="5gnidd", alpha=0.75, seed=0, beta=0.5): + odir = RESULTS / f"{dataset}__alpha{alpha:g}__seed{seed}__original_protean" + sdir = RESULTS / f"{dataset}__alpha{alpha:g}__seed{seed}__support_weighted__beta{beta:g}" + names = json.loads((odir / "metadata.json").read_text())["class_names"] + o = np.array(json.loads((odir / "test_confusion_matrix.json").read_text()), float) + s = np.array(json.loads((sdir / "test_confusion_matrix.json").read_text()), float) + diff = s - o + orow = np.where(o.sum(1, keepdims=True) == 0, 1, o.sum(1, keepdims=True)) + srow = np.where(s.sum(1, keepdims=True) == 0, 1, s.sum(1, keepdims=True)) + diff_rate = s / srow - o / orow + + fig, axes = plt.subplots(1, 2, figsize=(14.0, 6.4), constrained_layout=True) + vmax_c = np.abs(diff).max() + vmax_r = np.abs(diff_rate).max() * 100 + im0 = axes[0].imshow(diff, cmap="RdBu_r", + norm=TwoSlopeNorm(vcenter=0, vmin=-vmax_c, vmax=vmax_c)) + im1 = axes[1].imshow(diff_rate * 100, cmap="RdBu_r", + norm=TwoSlopeNorm(vcenter=0, vmin=-vmax_r, vmax=vmax_r)) + for ax in axes: + ax.set_xticks(range(len(names))) + ax.set_yticks(range(len(names))) + ax.set_xticklabels(names, rotation=45, ha="right", fontsize=8) + ax.set_yticklabels(names, fontsize=8) + ax.set_xlabel("predicted class") + axes[0].set_ylabel("true class") + axes[0].set_title("A. Difference in raw counts\n(proposed \u2212 original)") + axes[1].set_title("B. Difference in row-normalized rate\n(percentage points, proposed \u2212 original)") + # annotate the strongest cells on each panel + for ax, M, is_rate in ((axes[0], diff, False), (axes[1], diff_rate * 100, True)): + flat = [(abs(M[i, j]), i, j) for i in range(9) for j in range(9) if i != j] + for _, i, j in sorted(flat, reverse=True)[:6]: + v = M[i, j] + ax.text(j, i, f"{v:+.0f}" if not is_rate else f"{v:+.1f}", + ha="center", va="center", fontsize=6.4, fontweight="bold", + color="white" if abs(v) > (vmax_c if not is_rate else vmax_r) * 0.5 + else "#222222") + cb0 = fig.colorbar(im0, ax=axes[0], fraction=0.046, pad=0.02) + cb0.set_label("count delta") + cb1 = fig.colorbar(im1, ax=axes[1], fraction=0.046, pad=0.02) + cb1.set_label("row-rate delta (pp)") + dname = DATASET_NAME[dataset] + fig.suptitle(f"Confusion-matrix change: proposed (beta={beta:g}) minus original PROTEAN - " + f"{dname}, alpha={alpha:g}, seed={seed}, test", + fontsize=12, weight="bold", y=1.05) + fig.text(0.5, -0.02, + "Rows = true class, columns = predicted class; identical class order and sample counts (243,177) for both runs. " + "Diagonal: positive = more correct predictions; off-diagonal: positive = more errors of that (true\u2192predicted) type. " + "Red = positive delta (proposed has MORE samples in the cell); blue = negative delta (fewer). Values are descriptive only; no significance is inferred.", + ha="center", va="top", fontsize=8.4, color="#444444", linespacing=1.5) + _save(fig, f"figK_confusion_delta_{dataset}_a{alpha:g}_seed{seed}") + + +def main(): + _style() + figure_i() + figure_j() + figure_k() + + +if __name__ == "__main__": + main()