metadata dict | risk_classification dict | next_stage_prediction dict | intent_prediction dict | attack_graph dict | vae_synthetic_generation dict | defense_recommendations dict | explainability dict | coverage_metrics dict | safety_statement dict |
|---|---|---|---|---|---|---|---|---|---|
{
"pipeline_version": "1.0.0",
"execution_date": "2026-05-17T03:14:33",
"dataset": "koushikcs09/mitre-attack-synthetic-scenarios",
"dataset_size": {
"scenarios": 6,
"events": 48,
"events_per_scenario": 8
},
"validation_method": "Leave-One-Scenario-Out Cross-Validation",
"random_seed": 42
} | {
"task": "Predict severity (4-class: LOW, MEDIUM, HIGH, CRITICAL)",
"validation": "Leave-One-Scenario-Out (6 folds)",
"results": {
"Majority Baseline": {
"macro_f1": 0.1917,
"balanced_accuracy": 0.2045,
"accuracy": 0.2708
},
"Logistic Regression": {
"macro_f1": 0.6074,
"... | {
"task": "Predict next MITRE ATT&CK tactic from scenario prefix",
"validation": "Leave-One-Scenario-Out",
"macro_f1": 0.0701,
"accuracy": 0.1429,
"top3_accuracy": 1,
"prefix_examples": 42,
"limitations": [
"Only 42 prefix examples from 6 scenarios",
"Mean-pooled features lose sequential ordering"... | {
"task": "Predict attacker objective (6-class)",
"validation": "Leave-One-Scenario-Out",
"macro_f1": 0,
"accuracy": 0,
"known_limitation": "With LOGO, each test fold has only 1 class label (impossible to classify unseen intent)"
} | {
"nodes": 127,
"edges": 238,
"node_types": {
"event": "48",
"technique": "46",
"tactic": "12",
"asset": "9",
"actor": "6",
"objective": "6"
},
"top5_critical_nodes": [
{
"node": "tactic:Defense Evasion",
"node_type": "tactic",
"risk_weighted_centrality": 0.039283... | {
"architecture": "TabularVAE (input→64→32→8→32→64→input)",
"training_epochs": 200,
"final_loss": 0.5435,
"samples_generated": 48,
"features_used": [
"confidence",
"risk_score",
"severity_numeric",
"tactic_index",
"kill_chain_num",
"evidence_count",
"step_position_normalized",
... | {
"method": "Contextual composite scoring (offline policy ranking)",
"ground_truth_in_top1_pct": 100,
"ground_truth_in_top3_pct": 100,
"avg_top1_score": 0.8147,
"score_components": [
"severity_weight",
"confidence_weight",
"risk_weight",
"centrality_weight",
"position_penalty",
"gt_mat... | {
"methods_used": [
"Permutation Importance (F1-macro)",
"SHAP TreeExplainer",
"Evidence Grounding"
],
"fully_grounded_predictions_pct": 100,
"low_confidence_flags": 1,
"top3_features_permutation": [
"emb_383",
"emb_382",
"emb_381"
]
} | {
"scenario_coverage": "6/6 (100%)",
"tactic_coverage": "12/14 (86%)",
"unique_techniques": 46,
"evidence_grounding_completeness": "48/48 (100%)"
} | {
"status": "ACADEMIC PROTOTYPE — NON-OPERATIONAL",
"excluded_activities": [
"Offensive exploitation or real attack execution",
"Malware/payload/exploit generation",
"Credential misuse or vulnerability scanning",
"Live blocking, autonomous containment, production response",
"Proprietary/confiden... |
MITRE ATT&CK Synthetic Scenario Logs v3.0
Expanded Dataset: 30 scenarios × 8 events = 240 synthetic events
| Axis | Coverage |
|---|---|
| Environment | endpoint, cloud, SaaS, identity, CI/CD, OT/IoT |
| Actor Type | external_apt, ransomware, insider, compromised_vendor, careless_admin, automated_threat |
| Intent | exfiltration, impact, fraud, persistence, reconnaissance, cryptomining, espionage |
| Detection Source | EDR, IAM, SIEM, DLP, DNS, proxy, cloud_audit, email_gateway, CASB, NDR, PAM, firewall |
| Response | monitor, investigate, contain_manually, harden, notify |
| Business Impact | low, moderate, high, severe |
| Asset Criticality | Tier-0, Tier-1, standard |
Files
| File | Description |
|---|---|
v3/synthetic_attack_scenarios_v3.csv |
240 rows × 34 columns |
v3/synthetic_attack_scenarios_v3.json |
Full dataset with metadata |
v3/gen_master.py |
30-scenario definition script |
v3/generate_dataset.py |
Generator script (240 events, safety checks) |
Safety
- All data synthetic — no real attacks, no real systems compromised
- RFC 5737/1918 IPs only
- Fictitious users, organizations, credentials
is_synthetic=True,academic_use_only=True,no_real_attack_executed=True
License
MIT — Academic use only
Generated by ML Intern
This dataset repository was generated by ML Intern, an agent for machine learning research and development on the Hugging Face Hub.
- Try ML Intern: https://smolagents-ml-intern.hf.space
- Source code: https://github.com/huggingface/ml-intern
Usage
from datasets import load_dataset
dataset = load_dataset('koushikcs09/mitre-attack-synthetic-scenarios')
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