Datasets:

buckeyeguy
/

GraphIDS

license: cc-by-4.0
task_categories:
  - tabular-classification
  - graph-ml
tags:
  - intrusion-detection
  - CAN-bus
  - graph-neural-networks
  - knowledge-distillation
pretty_name: GraphIDS Paper Data

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:

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

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