Aria AI — Cybersecurity
Collection
Evidence-linked cybersecurity review demos. Analyst workstations, not automatic enforcement. • 27 items • Updated
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has_ground_truth bool | input_events int64 | cluster_count int64 | baseline_queue_size int64 | noise_reduction float64 | unwitnessed_claim_rate float64 | precision_at_k float64 | pairwise_precision float64 | important_pairwise_recall float64 | important_incident_recall float64 | top_k int64 |
|---|---|---|---|---|---|---|---|---|---|---|
true | 643 | 92 | 48 | 0.8569 | 0 | 0.875 | 0.8578 | 0.8034 | 1 | 8 |
AriaSOC laboratory cases
Synthetic SIEM alerts and case labels used by AriaSOC.
Collection: Aria AI — Cybersecurity. Live Gradio hosting follows the personal-PRO / organization-card pattern used by the Aria AI demos.
This is generated data (seed 24). It is for reproducible demos and tests, not a customer extract. Addresses are documentation-range or RFC1918 laboratory values. Tokens and mailboxes are fake and redacted in the demo.
Files
| File | Grain | Notes |
|---|---|---|
data/alerts.csv |
one alert | Required SOC fields plus source, title, optional ATT&CK |
data/cases.csv |
20 generator scenarios | Incident id, importance, gold label |
data/splits.json |
time cut | Train = earlier 70% of timestamps |
data/evaluation.json |
one run | Recorded baseline vs correlation metrics |
data/sample_preview.json |
UI snapshot | Precomputed KPI HTML; labeled as a sample |
Schema
alerts.csv: event_id, timestamp, asset_id, rule_id, severity, user_id, source, title, message, src_ip, dest_host, process, technique, case_id, incident_id, importance, gold_label
Required for the demo parser: event_id, timestamp, asset_id, rule_id, severity, user_id. Empty asset_id or user_id is valid and means unknown, not a pass.
Sources: identity, edr, network.
Importance: important, benign, noise, insufficient.
Split
Do not shuffle rows of the same incident into train and test. Use timestamp <= train_until from splits.json as history. The optional LightGBM ranker is fit only on that earlier window.
Generator
projects/aria-soc/src/generator.py in the Space repo, seed 24. Twenty scenarios include a finance compromise chain, look-alike benign activity, missing-schema events, and a two-day-old recon burst that sits outside the default correlation window.
License
CC-BY-4.0. Keep the synthetic-data label if you reuse the tables.
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