---
pretty_name: "Dendroaspis Tetragon HIDS (Agent-Driven Attack Dataset)"
license: cc-by-4.0
task_categories:
  - tabular-classification
  - other
tags:
  - cybersecurity
  - host-intrusion-detection
  - anomaly-detection
  - tetragon
  - ebpf
  - sequence-modeling
  - mitre-attack
  - agentic-ai
  - autonomous-agents
  - red-teaming
  - time-series
size_categories:
  - 10M<n<100M
language:
  - en
---

# Dendroaspis Tetragon HIDS — Agent-Driven Attack Dataset

**22 technique IDs (21 ATT&CK + 1 supply-chain capstone) · 19.3 M events · 6-day controlled collection window**

A behavioral-event corpus collected from a single Linux LXC container under a
controlled Atomic Red Team (ART) campaign, parsed and feature-engineered with
a Tetragon-native pipeline. Released for:

- host-anomaly-detection research
- sequence modeling (Mamba, Transformers, etc.)
- ATT&CK-technique-detectability studies
- **autonomous-attacker behavioral analysis** — see below

The full provenance is in
[`datacard.md`](./datacard.md) and [`datasheet.md`](./datasheet.md).

---

## 🤖 Agent-driven attack execution

The **test set** consists of events generated during a controlled penetration-testing
campaign that was **fully orchestrated by an AI agent**.

The agent was responsible for:

- installing and configuring Atomic Red Team (ART)
- performing host-level reconnaissance
- selecting applicable ATT&CK techniques
- constructing the attack campaign
- executing the techniques within the target environment

This is a fundamental shift from traditional HIDS datasets:

| Dataset type             | Execution model                     |
|--------------------------|-------------------------------------|
| Traditional HIDS corpora | Human-scripted attacks              |
| This dataset             | **Agent-driven attack orchestration** |

### Operational model

The dataset captures an end-to-end autonomous attack loop:

> Observation → Reconnaissance → Planning → Tooling → Execution → Iteration

The resulting telemetry reflects not only attack techniques, but the
**behavioral footprint of an autonomous attacker operating in a real system**.

### Why this matters

Agent-driven attacks introduce:

- structured but adaptive execution patterns
- tool installation and environment-preparation behavior
- continuous recon → execution feedback loops
- multi-stage dependency chains across techniques

This enables research into:

- detection of **autonomous-attacker behavior**
- distinguishing human vs agent execution patterns
- modeling attacks as **sequences of intent**
- identifying **planning and coordination signals** in telemetry

---

## Capstone: agent-driven supply-chain attack (`CHAIN.CAP01`)

In addition to 21 individual ATT&CK techniques, the dataset includes a
**capstone scenario** simulating a malicious npm supply-chain compromise,
inspired by real-world self-propagating npm worms (e.g., the 2025
**Shai-Hulud** incident).

In this scenario, the agent:

- installs a dependency via `npm install`
- triggers execution through package lifecycle scripts (e.g. `postinstall`)
- performs local reconnaissance and file access
- executes follow-on actions including potential credential access and exfiltration

This produces a **multi-stage attack chain compressed into a short execution
window** (≈14 seconds, 34,130 events), labeled `CHAIN.CAP01`. Unlike the
individual ATT&CK techniques, the capstone is a **coordinated attack
narrative** combining:

- execution
- discovery
- credential access
- defense evasion
- exfiltration behaviors

within a single autonomous workflow.

### Why the capstone matters

This scenario reflects a realistic modern attack vector:

- software supply-chain compromise
- developer-environment targeting
- automated post-install exploitation

and offers a unique opportunity to study:

- **cross-technique behavioral chaining**
- **short-duration, high-density attack bursts**
- **agent-driven multi-stage execution patterns**

---

## Splits at a glance

| Split | Rows       | Collection window (UTC)†                       | Attack rate |
|-------|------------|------------------------------------------------|-------------|
| train | 16,951,950 | 2026-04-14 01:41 → 2026-04-19 22:09            | 0.0 %       |
| test  |  2,349,965 | 2026-04-19 21:46 → 2026-04-20 02:01            | 30.0 %      |

All ART attack intervals fall in the **test** split. Train is benign-only;
unsupervised / self-supervised methods are the natural fit.

**† Pre-window event timestamps.** ≈295 events (267 train + 28 test, 0.0015 %
of the corpus) carry `event_time` values that predate the stated collection
window. All have `f_is_procfs_walk = 1`: parser-synthesized `process_exec`
records for daemons (`systemd`, `bash`, `dbus-daemon`, `polkitd`, `cron`,
`sshd`, etc.) that were already running when Tetragon collection started,
using the daemon's original `process_start_time` as the event timestamp.
They preserve lineage roots for downstream events. Filter
`WHERE f_is_procfs_walk = 0` to exclude them from time-window analysis.

---

## Top techniques by event volume (test split)

| ID          | Technique                                                  | Test events |
|-------------|------------------------------------------------------------|-------------|
| T1059.004   | Unix Shell                                                 | 175,797     |
| T1083       | File and Directory Discovery                               | 154,581     |
| T1552.001   | Credentials In Files                                       |  97,493     |
| T1543.002   | Systemd Service                                            |  47,901     |
| CHAIN.CAP01 | Capstone Chain v0.1 (Supply-Chain Narrative)               |  34,130     |
| T1548.003   | Sudo and Sudo Caching                                      |  24,229     |
| T1059.006   | Python                                                     |  22,675     |
| T1562.001   | Disable or Modify Tools                                    |  18,610     |
| T1195.002   | Compromise Software Supply Chain                           |  15,703     |
| T1548.001   | Setuid and Setgid                                          |  12,461     |

Full per-technique table (22 IDs across 8 ATT&CK tactics) lives in
[`labels.csv`](./labels.csv) and [`datacard.md`](./datacard.md).

---

## Headline numbers (Run 2, post-rebuild)

| Scorer                              | Test AUROC |
|-------------------------------------|------------|
| Mamba MEM-FA (field-aware)          | **0.7513** |
| Mamba NLL (autoregressive)          | 0.7411     |
| IsolationForest (best classical)    | 0.6658     |
| n-gram (token-level)                | 0.5672     |
| XGBoost                             | 0.5000     |

Mamba's sequence stack adds ≈0.085 AUROC over the strongest classical
baseline. See [`datacard.md`](./datacard.md) for the per-technique breakdown
and the per-window inversion analysis on unsupervised classicals.

---

## Quick start

```python
from datasets import load_dataset
import pandas as pd

ds = load_dataset("rypow/dendroaspis-tetragon-hids", streaming=True)

event = next(iter(ds["train"]))
print(event["f_event_type"], event["f_action_family"], event["token"])

labels = pd.read_csv("labels.csv", parse_dates=["start_ts", "end_ts"])
labels["start_ts"] = labels["start_ts"].dt.tz_convert("UTC")
labels["end_ts"]   = labels["end_ts"].dt.tz_convert("UTC")
print(f"{len(labels)} intervals · {labels['technique_id'].nunique()} techniques")
```

To interpret the integer-coded categorical columns, load
[`reproduction/feature_definitions.json`](./reproduction/feature_definitions.json) — every code (e.g. `f_action_family=6`)
maps to a label and a one-line semantic description.

---

## Anonymization

The released parquet has been fully anonymized via a component-first deep
aliasing pass:

- **Basenames** (`proc_binary_basename`, `parent_binary_basename`,
  `root_ancestor_basename`): an allowlist of distro / dev-tool basenames
  passes through verbatim (`bash`, `node`, `python3.12`, `claude`, `gh`,
  `npm`, `code`, `git`, …); custom binaries become `custom_tool_NNN`;
  VSCode commit-hash binaries collapse to `vscode_server_bin`.
- **`token`** and **`parent_child_pair`** are regenerated from the aliased
  basenames via the canonical `format_token` (so the original basename never
  re-enters these composite columns).
- **`proc_cwd_normalized`** is path-rewritten (`/home/<user>` →
  `/home/_user_`, `/mnt/projects/<X>` → `/workspace/_project_`, etc.).
- **`proc_exec_id`** and **`process_tree_root_exec_id`** are pseudonymized
  via salted HMAC-SHA256 to `exec_<32hex>`. Equality joins are preserved
  (lineage walks still work).
- A defensive global string sweep substitutes `rypow → research_user`,
  `prox1 → sandbox_host`, `dendroaspis → agent_platform_v02`,
  `ryan_admin → privileged_user` across every string column.

Post-write assertions confirm zero PII hits across raw and base64-decoded
forms of all 12 string columns. Full alias map at
[`reproduction/alias_map.json`](./reproduction/alias_map.json); the realised
PASS set at [`reproduction/allowlist_basenames.txt`](./reproduction/allowlist_basenames.txt).

See [`datacard.md`](./datacard.md) "Personal and sensitive information" for
the per-column decision log.

---

## Citation

```bibtex
@dataset{dendroaspis_v0_2_2026,
  title   = {Dendroaspis Tetragon HIDS Dataset (Agent-Driven Attack Variant)},
  author  = {Powers, Ryan William},
  year    = {2026},
  version = {0.2.1}
}
```

The canonical BibTeX is also at [`dendroaspis.bib`](./dendroaspis.bib);
update the `url` and `doi` fields once those are minted.

---

## Pointers

- **Full feature documentation:** [`datacard.md`](./datacard.md)
- **Provenance + collection process:** [`datasheet.md`](./datasheet.md)
- **Reproducing collection:** [`reproduction/REPRODUCE.md`](./reproduction/REPRODUCE.md)
- **Integer-code → label tables:** [`reproduction/feature_definitions.json`](./reproduction/feature_definitions.json)
- **Anonymizer alias map:** [`reproduction/alias_map.json`](./reproduction/alias_map.json)
- **Per-file integrity:** [`SHA256SUMS`](./SHA256SUMS), machine manifest at [`RELEASE.json`](./RELEASE.json)
- **Github:** https://github.com/ryypow/dendroaspis

---

## License

CC BY 4.0 — see [`LICENSE`](./LICENSE). Code under
`reproduction/scripts/` (in the project repository) is MIT-licensed.

## Intended use + dual-use disclaimer

Defensive research only. The attack telemetry is real ATT&CK-technique
execution but generated in an isolated lab LXC container; the corpus is
**not** an attack template, **not** a payload library, and **not** a
substitute for production EDR data. Operators who reuse the bundled tracing
policies + parser to collect their own corpus are responsible for their own
sandboxing.

See [`datacard.md`](./datacard.md) "Intended use + dual-use disclaimer" for
the full statement.