---
license: cc-by-4.0
task_categories:
  - tabular-classification
  - time-series-forecasting
language:
  - en
tags:
  - cybersecurity
  - intrusion-detection
  - network-security
  - edge-computing
  - threat-intelligence
pretty_name: HookProbe Edge IDS Threat Telemetry
size_categories:
  - 100K<n<1M
configs:
  - config_name: verdicts
    data_files:
      - split: all
        path: "data/*.parquet"
  - config_name: aggregated
    data_files:
      - split: daily
        path: "aggregated/daily_country_asn.parquet"
---

# HookProbe Edge IDS Threat Telemetry

Real-world, anonymised threat verdicts from the **HookProbe** production
edge intrusion-detection system. Unlike synthetic lab datasets
(CICIDS2017, UNSW-NB15, Kitsune) this is what an actual edge sensor
mesh observes on the open internet, labelled by the SENTINEL ensemble
(isolation forest + calibrated naive-Bayes) that ships with HookProbe.

- **Sensor**: Raspberry Pi edge node + NAPSE AI-native flow classifier
- **Enrichment**: RDAP country + ASN lookups
- **Labels**: `malicious` / `suspicious` / `benign` with anomaly score 0–1
- **Actions**: `block` / `throttle` / `cognitive_block` / `cognitive_throttle` /
  `escalate` / `alert` / `monitor` / `cognitive_none` / `none` — emitted by the
  CNO cognitive-defense layer. `cognitive_*` variants indicate the decision came
  from the CNO synaptic controller rather than the static HYDRA rule path.
- **License**: CC-BY-4.0 — free for commercial or academic use with attribution
- **Canonical URL**: https://hookprobe.com/threats/

## Schema

### `verdicts` (primary)

| column          | type          | description                                             |
|-----------------|---------------|---------------------------------------------------------|
| timestamp_hour  | timestamp[s]  | UTC, truncated to the hour                              |
| src_ip_hash     | string(16)    | SHA-256(salt ∥ src_ip)[:16]. Pseudonymous.              |
| country         | string        | ISO-3166-1 alpha-2                                      |
| asn             | uint32        | Autonomous-system number (0 if unknown)                 |
| asn_name        | string        | ASN organisation name                                   |
| anomaly_score   | float32       | Ensemble output, 0.0 benign – 1.0 malicious             |
| verdict         | enum          | `malicious` \| `suspicious` \| `benign`                |
| action_taken    | enum          | see **Actions** above — 9 possible values               |

### `aggregated` (derived)

| column            | type     | description                                      |
|-------------------|----------|--------------------------------------------------|
| date              | date32   | UTC day                                          |
| country, asn, asn_name | ...  | same semantics as primary                        |
| threat_count      | uint32   | total verdicts for that country/asn/day          |
| malicious, suspicious, benign | uint32 | per-class counts                         |
| avg_anomaly_score | float32  | mean ensemble score                              |

## Privacy model

- **IPs are hashed** with a project salt that is not published. The hash is
  deterministic across releases so longitudinal analysis of attacker
  behaviour is preserved, but the mapping is one-way.
- **Timestamps are truncated to the hour** to prevent correlation against
  third-party logs with second-precision timestamps.
- **No payload data** is exposed — only verdicts and enrichment.

## Data caveats

- The **SENTINEL calibration window (2026-02-22 → 2026-03-09)** is
  excluded by default. During that period the ensemble produced a ~98%
  false-positive rate, which was fixed in the 9-point SENTINEL release
  on 2026-03-09. Training on that window would mis-lead downstream models.
- Because the sensor is a single edge node, geographic and ASN
  distributions reflect **what targets that specific deployment** — not
  a global baseline. Treat skew accordingly.

## Citation

```bibtex
@dataset{hookprobe_edge_ids_threats_2026,
  author  = { {HookProbe Security Research} },
  title   = { {HookProbe Edge IDS Threat Telemetry} },
  year    = { 2026 },
  url     = { https://huggingface.co/datasets/hookprobe/edge-ids-threats },
  note    = { 548,428 verdicts; temporal coverage 2026-04 → 2026-04 },
}
```

## Example usage

```python
from datasets import load_dataset

# Full verdict stream — ML-grade labelled data
ds = load_dataset("hookprobe/edge-ids-threats", name="verdicts", split="all")
print(ds[0])

# Pre-aggregated daily country/ASN counts — analyst-grade
agg = load_dataset("hookprobe/edge-ids-threats", name="aggregated", split="daily")
```

## Updates

- **Cadence**: new month appended at 02:00 UTC on the 1st of each month
- **Major releases**: quarterly, with a companion report at
  https://hookprobe.com/blog/threat-landscape-<year>-q<n>/

## Contact

- Website: https://hookprobe.com
- Security research: qsecbit@hookprobe.com
- Issues: open a discussion on this dataset's HF page