GraphIDS — Paper Data

Evaluation artifacts for "Adaptive Fusion of Graph-Based Ensembles for Automotive Intrusion Detection".

Consumed by kd-gat-paper via data/pull_data.py.

Schema Contract

If you change column names or file structure, pull_data.py will fail. The input schema is enforced in pull_data.py:INPUT_SCHEMA.

metrics.parquet

Per-model evaluation metrics across all runs.

Column	Type	Description
run_id	str	Run identifier, e.g. hcrl_sa/eval_large_evaluation
model	str	Model name: gat, vgae, fusion
accuracy	float	Classification accuracy
precision	float	Precision
recall	float	Recall (sensitivity)
f1	float	F1 score
specificity	float	Specificity (TNR)
balanced_accuracy	float	Balanced accuracy
mcc	float	Matthews correlation coefficient
fpr	float	False positive rate
fnr	float	False negative rate
auc	float	Area under ROC curve
n_samples	float	Number of evaluation samples
dataset	str	Dataset name: hcrl_sa, hcrl_ch, set_01–set_04

embeddings.parquet

2D UMAP projections of graph embeddings per model.

Column	Type	Description
run_id	str	Run identifier
model	str	Model that produced the embedding: gat, vgae
x	float	UMAP dimension 1
y	float	UMAP dimension 2
label	int	Ground truth: 0 = normal, 1 = attack

cka_similarity.parquet

CKA similarity between teacher and student layers (KD runs only).

Column	Type	Description
run_id	str	Run identifier (only *_kd runs)
dataset	str	Dataset name
teacher_layer	str	Teacher layer name, e.g. Teacher L1
student_layer	str	Student layer name, e.g. Student L1
similarity	float	CKA similarity score (0–1)

dqn_policy.parquet

DQN fusion weight (alpha) per evaluated graph.

Column	Type	Description
run_id	str	Run identifier
dataset	str	Dataset name
scale	str	Model scale: large, small
has_kd	int	Whether KD was used: 0 or 1
action_idx	int	Graph index within the evaluation set
alpha	float	Fusion weight (0 = full VGAE, 1 = full GAT)

Note: Lacks per-graph label and attack_type. The paper figure needs these fields joined from evaluation results. This is a known gap — see pull_data.py skip message.

recon_errors.parquet

VGAE reconstruction error per evaluated graph.

Column	Type	Description
run_id	str	Run identifier
error	float	Scalar reconstruction error
label	int	Ground truth: 0 = normal, 1 = attack

Note: Single scalar error — no per-component decomposition (Node Recon, CAN ID, Neighbor, KL). The paper figure needs the component breakdown. This is a known gap.

attention_weights.parquet

Mean GAT attention weights aggregated per head.

Column	Type	Description
run_id	str	Run identifier
sample_idx	int	Graph sample index
label	int	Ground truth: 0 = normal, 1 = attack
layer	int	GAT layer index
head	int	Attention head index
mean_alpha	float	Mean attention weight for this head

Note: Aggregated per-head, not per-edge. The paper figure needs per-edge attention weights with node positions. This is a known gap.

graph_samples.json

Raw CAN bus graph instances with node/edge features.

Top-level keys: schema_version, exported_at, data, feature_names.

Each item in data:

• dataset: str — dataset name
• label: int — 0/1
• attack_type: int — attack type code
• attack_type_name: str — human-readable name
• nodes: list of {id, features, node_y, node_attack_type, node_attack_type_name}
• links: list of {source, target, edge_features}
• num_nodes, num_edges: int
• id_entropy, stats: additional metadata

metrics/*.json

Per-configuration evaluation results. 18 files covering 6 datasets x 3 configs (large, small, small_kd).

Each file: {schema_version, exported_at, data: [{model, scenario, metric_name, value}]}

Currently only contains val scenario — cross-dataset test scenarios are not yet exported.

Other files

File	Description
leaderboard.json	Cross-dataset model comparison (all metrics, all runs)
model_sizes.json	Parameter counts per model type and scale
training_curves.parquet	Loss/accuracy curves over training epochs
graph_statistics.parquet	Per-graph structural statistics
datasets.json	Dataset metadata
runs.json	Run metadata
kd_transfer.json	Knowledge distillation transfer metrics

Run ID Convention

Format: {dataset}/{eval_config}

• Datasets: hcrl_sa, hcrl_ch, set_01 through set_04
• Configs: eval_large_evaluation, eval_small_evaluation, eval_small_evaluation_kd

The paper defaults to hcrl_sa/eval_large_evaluation for main results and hcrl_sa/eval_small_evaluation_kd for CKA.

Known Gaps

These files need richer exports from the KD-GAT pipeline:

1. dqn_policy.parquet — needs per-graph label + attack_type columns
2. recon_errors.parquet — needs per-component error decomposition
3. attention_weights.parquet — needs per-edge weights + node positions
4. metrics/*.json — needs cross-dataset test scenario results

Until these are addressed, pull_data.py preserves existing committed files for the affected figures.