chore: checkpoint PROTEAN research planning and architecture

configs/models/protean_cnn.yaml

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+# PROTEAN detection model as described in Chennoufi et al. (2025), Section 4.1.
+# The architecture is FROZEN to the baseline and is not part of this study's
+# contribution. The only proposed change is server-side prototype aggregation.
+
+model:
+  name: protean_cnn
+  framework: pytorch
+  input: flow_features          # tabular -> treated as 1D / single-channel input
+  embedding_dim: 128            # output of the dense layer == prototype dimension d
+  layers:
+    - type: conv1d
+      filters: 64
+      activation: relu
+      pool: max
+      dropout: 0.2
+    - type: conv1d
+      filters: 128
+      activation: relu
+      pool: max
+      dropout: 0.5
+    - type: flatten
+    - type: dense
+      units: 128
+    - type: classification_head
+      output: log_softmax
+  # Head concatenates/uses the dense embedding; EOF prototypes are extracted
+  # from the embedding part phi(x) (first 8 of 10 layers per the paper text).
+  prototype_source: embedding
+
+loss:
+  # Eq. (3): classification loss + lambda * prototype alignment + mu/2 * model alignment
+  terms:
+    - classification: cross_entropy
+    - prototype_alignment: l2_squared_distance_to_global_prototype
+      weight: lambda
+    - model_alignment: l2_squared_distance_to_previous_global_model
+      weight: mu
+  mu: 0.1
+  lambda: null                  # UNRESOLVED: not stated in the paper text
+
+inference:
+  rule: nearest_global_prototype_l2
+  trailing: "assign label of the global prototype with the smallest L2 distance
+              to the instance embedding (paper Section 3.2)"