Datasets:

ClarusC64
/

cascade-multi-ai-cyber-infrastructure-economy-disruption-v0.1

scenario_id string	ai_misuse_capability float64	attacker_scale float64	exploit_chain_complexity float64	infrastructure_dependency_depth float64	service_outage_hours int64	economic_loss_index float64	incident_detection_latency_days int64	patch_coordination_lag_days int64	government_response_lag_days int64	public_trust_index float64	regulatory_pressure_score float64	buffer_infra_redundancy float64	buffer_response_capacity float64	cascade_severity_score float64	label_cascade_event int64
CYB-001	0.28	0.3	0.34	0.48	6	0.18	5	12	18	0.72	0.31	0.74	0.71	0.33	0
CYB-002	0.76	0.78	0.79	0.86	72	0.74	14	38	52	0.34	0.82	0.29	0.33	0.92	1
CYB-003	0.44	0.46	0.5	0.62	18	0.34	8	20	29	0.6	0.48	0.58	0.6	0.55	0
CYB-004	0.84	0.86	0.88	0.91	96	0.82	18	47	61	0.28	0.9	0.22	0.25	0.96	1
CYB-005	0.58	0.62	0.63	0.73	36	0.49	10	27	40	0.49	0.63	0.46	0.5	0.7	0
CYB-006	0.22	0.24	0.28	0.41	4	0.12	4	10	15	0.76	0.26	0.79	0.75	0.28	0
CYB-007	0.92	0.9	0.93	0.94	120	0.9	21	55	70	0.2	0.94	0.18	0.2	0.99	1
CYB-008	0.47	0.5	0.52	0.66	22	0.38	9	22	32	0.57	0.52	0.55	0.58	0.59	0
CYB-009	0.8	0.82	0.84	0.89	80	0.78	16	42	58	0.31	0.86	0.26	0.29	0.94	1

What this repo does

This dataset tests whether a model can detect a cross-domain cascade where AI-enabled cyber pressure propagates into infrastructure outages and economic disruption.

You provide structured signals describing:

AI misuse capability and attacker scale
exploit chain complexity and infrastructure dependency depth
detection and patch coordination lag
outage duration and economic loss pressure
trust, regulation, and buffer capacity

The model predicts whether the scenario crosses into a cascade event.

Core cascade

Four interacting systems:

AI misuse

ai_misuse_capability
attacker_scale

Cyber pathway

exploit_chain_complexity
incident_detection_latency_days

Infrastructure impact

infrastructure_dependency_depth
service_outage_hours

Economic and policy feedback

economic_loss_index
government_response_lag_days

Prediction target

Target column:

label_cascade_event

Meaning:

0 = disruption remains containable
1 = cross-system cascade propagates across infrastructure and economy

Row structure

Each row is a scenario snapshot.

Key columns:

ai_misuse_capability
attacker_scale
exploit_chain_complexity
infrastructure_dependency_depth
service_outage_hours
economic_loss_index
incident_detection_latency_days
patch_coordination_lag_days
government_response_lag_days
public_trust_index
regulatory_pressure_score
buffer_infra_redundancy
buffer_response_capacity
cascade_severity_score

Files

data/train.csv
10-line labeled sample
data/tester.csv
10-line labeled sample
scorer.py
Binary metrics and confusion matrix

Evaluation

Run:

python scorer.py --gold data/tester.csv --pred your_predictions.csv

Outputs:

accuracy
precision
recall
f1
confusion matrix

License

MIT

This dataset identifies a measurable coupling pattern associated with systemic instability. The sample demonstrates the geometry. Production-scale data determines operational exposure.

What Production Deployment Enables • 50K–1M row datasets calibrated to real operational patterns • Pair, triadic, and quad coupling analysis • Real-time coherence monitoring • Early warning before cascade events • Collapse surface and recovery window modeling • Integration and implementation support Small samples reveal structure. Scale reveals consequence.

Enterprise & Research Collaboration Clarus develops production-scale coherence monitoring infrastructure for critical systems across healthcare, finance, infrastructure, and regulatory domains. For dataset expansion, custom coherence scorers, or deployment architecture: team@clarusinvariant.com

Instability is detectable. Governance determines whether it propagates.

Downloads last month: 17

Number of rows:

Total file size:

9.7 kB