# Datasheet — v0.2 Tetragon-native HIDS corpus Following the [Datasheets for Datasets](https://arxiv.org/abs/1803.09010) framework (Gebru et al.). Cross-references the [`datacard.md`](./datacard.md) for numerical breakdowns; this document covers process and provenance. --- ## 1. Motivation ### Why was this dataset created? To support an academic project investigating distributed Mamba state-space models for host-telemetry LLM-orchestrated attack detection. ### Who created this dataset and on behalf of which entity? Ryan William Powers, in the context of an academic course project. No commercial entity is involved; collection happened on the author's own Linux host. ### Who funded the creation of the dataset? Self-funded. ### Any other comments? The release is contingent on the author's anonymization sweep: per-column decisions documented in the datacard's "Personal and sensitive information" section); no third-party identifiable data was captured because no third-party traffic crosses the isolated LXC container. --- ## 2. Composition ### What do the instances represent? Each instance is a single **OS event** — one row of the parquet — emitted by Tetragon eBPF probes inside an LXC container during normal developer activity or during a labeled ATT&CK technique trial. Events fall into three top-level types: `process_exec`, `process_exit`, and `process_kprobe` (with the kprobe sub-type captured in `f_kprobe_function`). ### How many instances are there? - Train: 16,951,950 events (no attack labels by design) - Test: 2,349,965 events (705,852 inside labeled-attack windows ≈ 30 %) - Total: 19,301,915 events ### Does the dataset contain all possible instances? No. The dataset is a **time-bounded sample** from one host between 2026-04-14T01:41Z and 2026-04-20T02:01Z. Only events the parser admits (post container filter, post sentinel-window exclusion) reach the parquet. Procfs-walk synthetic execs are **tagged** (via `f_is_procfs_walk = 1`) rather than dropped, so they are present in the release; filter on the flag if needed. ### What does each instance consist of? A 63-column row mixing: - 22 integer-coded categoricals (`f_event_type`, `f_action_family`, etc., including the four 3a′ promoted categoricals: `f_path_category`, `f_dst_ip_category`, `f_dst_port_category`, `f_object_category`) - 4 string-coded categoricals (`path_category`, `object_category`, `dst_ip_category`, `dst_port_category` — provenance side columns) - 7 hash columns (256–4096 buckets each) - 6 boolean flags (`f_in_init_tree` and `f_args_truncated` were dropped in 3a′ as constant-zero in this corpus) - 5 numeric columns (timestamps in ns, capability bitmasks, fd integer) - 3 bucketed log-time columns - 3 identifier columns (`event_time`, `proc_exec_id`, `proc_pid`) - 7 release-eligible side strings (basenames, normalized cwd, `parent_child_pair`, `token`, `process_tree_root_exec_id`) Full schema and integer-code → label mapping in `reproduction/feature_definitions.json` (schema version 1.1). ### Is there a label or target associated with each instance? Not directly. **Time-window labels** in `labels.csv` provide `(host, technique_id, technique_name, tactic, start_ts, end_ts, trial, delivery, notes)` per ART trial. To label an instance, join on `event_time >= start_ts AND event_time < end_ts` (half-open interval — matches the audit notebook); the event inherits the intersected interval's technique label. Events outside any interval are benign by exclusion. ### Is any information missing from individual instances? Yes, by design: - Raw paths (`proc_binary`, `proc_cwd`, `parent_*`, `kp_*_path`) are dropped. - Raw arguments (`proc_arguments`, `parent_arguments`, `kprobe_args_json`) are dropped. - Raw IPs (`kp_sock_daddr`, `kp_sock_saddr`) are dropped. - Raw uids and namespace inums are dropped. - Hostnames (`node_name`) are dropped. - `proc_exec_id` and `process_tree_root_exec_id` are pseudonymized to `exec_<32hex>` via salted HMAC-SHA256; equality joins are preserved (lineage walks still work) but the original `host:ts:pid` payload is erased. - Basenames (`proc_binary_basename`, `parent_binary_basename`, `root_ancestor_basename`) are aliased: distro / dev-tooling pass through verbatim, custom binaries become `custom_tool_NNN`, VSCode commit-hash binaries collapse to `vscode_server_bin`. Full alias map at `reproduction/alias_map.json`. These are reachable in the original raw parquet (not released; see §6 distribution) and are reconstructible from the bundled parser + tracing configs by re-collecting one's own data. ### Are relationships between individual instances made explicit? Yes — process lineage is preserved through three columns: - `proc_exec_id` — pseudonymized but stable per-exec identifier. - `f_lineage_depth` + `f_lineage_bag_hash` — encoder-side ancestry. - `process_tree_root_exec_id` + `f_root_ancestor_basename_hash` — root of the process subtree. ### Are there recommended data splits? Yes. **Use the chronological train/test split as shipped.** All ART attack intervals fall in test; train is benign-only by construction. This is the split the v0.2 paper reports on. ### Is the dataset self-contained? Almost. The parquet shards + `labels.csv` + `feature_definitions.json` are the canonical artifact. `reproduction/` contains the configs. ### Does the dataset contain confidential / offensive content? No confidential third-party data — collection happened on an isolated LXC container with no external user traffic. ATT&CK technique execution is by construction adversarial-style activity but happened only against the author's own container. An anonymizer further de-identifies the released parquet (basename aliasing, path rewriting, exec-id pseudonymization, defensive global string sweep — see `datacard.md` "Personal and sensitive information"). --- ## 3. Collection process ### How was the data acquired? - **Sensor:** Tetragon 1.6.1 (Cilium eBPF) running as a host-level daemon. - **Target:** a single LXC container on the host. The parser pins on the container's PID-namespace inum at filter time. - **Tracing policies:** 7 YAML policies covering file (fd_install, do_unlinkat, chmod_common), network (tcp_connect / close, inet_csk_accept, udp_sendmsg), memory (security_mmap_file, security_file_mprotect), privilege (commit_creds), process injection (sys_ptrace, sys_process_vm_writev), and file-attribute hooks. Bundled in `reproduction/tetragon-configs/`. - **Output format:** rotated `events-*.json.gz` files written to `/var/log/tetragon/` and rsynced to a NAS for archival. ### Is the data sample of a larger set? Yes — a time-bounded slice of the host's Tetragon stream during the collection window. Earlier and later data from the same host exists but is not part of this release. ### Who collected the data, and how were they compensated? The author. No compensation; this is an academic course project. ### Over what timeframe was the data collected? 2026-04-14T01:41:13Z (first event in train) → 2026-04-20T02:01:27Z (last event in test). 6-day collection window, with a brief Tetragon outage on 2026-04-18→19 (documented in the project repo's incident log) that triggered a PID-namespace inum rotation captured in the multi-inum `target_pid_ns_inum` filter. ### Were any ethical-review processes conducted? No formal IRB. The dataset captures only the author's own activity on the author's own hardware. ### Were the individuals notified / did they consent? The dataset captures the author's own session; the author is the sole subject and consents to the release --- ## 4. Preprocessing / cleaning / labeling ### Was preprocessing / cleaning done? Yes. Two stages: 1. **Parser** (`tetragon_native_parser.py`): - Container filter: drops events outside the target PID-namespace inum set. - Sentinel-window filter: drops events inside four ART setup / dry-run / smoke-test intervals on 2026-04-19. - Procfs-walk: synthetic execs from Tetragon's startup /proc/ scan are **tagged** (`f_is_procfs_walk = 1`), not dropped — they preserve lineage roots for daemons that pre-existed the agent. - Field normalization: parses RFC3339-ns timestamps to int64 ns, hydrates 126 typed columns from the JSONL envelopes. 2. **Behavior builder** (`v0_2_behavior_builder.py`): - Lineage walk to populate `f_lineage_*` and `process_tree_*` columns. Train→test seeding closes daemon-rooted parents that lived only in the train window. - Action-family / object-category / path-category / dst-ip-category / dst-port-category derivations. - Hashing of basenames / lineage bags / parent-child pairs into fixed-capacity buckets (3a′ caps: 2048 / 1024 / 4096 / 256 / 1024 / 1024 / 4096). - Categorical encoding of every kept column to `uint8` / `uint16`. - Per-process `delta_t_prev_ns` keying (eliminates global-stream ordering artifacts). 3. **Anonymization** (`scripts/anonymize_v0_2_parquet.py`, run after the builder): basename aliasing, path rewriting, composite re-composition, exec-id salted-HMAC pseudonymization, defensive global string sweep. See the datacard "Personal and sensitive information" section. ### Was the "raw" data saved? Yes, internally. The 126-column raw parquet (`raw_train.parquet`, `raw_test.parquet`) is not part of the public release because it would require column-level scrubbing across 9 path-bearing columns spanning 60–95 % of rows. Re-collection from the bundled configs is the supported path for raw data. ### Is the preprocessing software available? Yes. Both stages live in the project repository at the parser SHA pinned in `reproduction/PARSER_VERSION`. The repo (linked from the [Hugging Face dataset card](./datacard.md)) is permissively licensed (MIT). - **https://github.com/ryypow/dendroaspis** --- ## 5. Uses ### Has the dataset been used for any tasks? Yes: - **Mamba NLL (autoregressive, Run 2)** — next-event surprise scorer with the front-pad alignment fix; test AUROC = 0.7411. - **Mamba MEM-FA (field-aware masked-event modeling, Run 2)** — per-field categorical CE heads on top of the encoded representation; test AUROC = 0.7513 (best Mamba result on this corpus). - **Classical-baseline bake-off** — n-gram (token-level), IsolationForest, XGBoost, all trained on the same encoded feature columns. Best classical: IsolationForest (AUROC 0.6658). Mamba's stack adds ≈0.085 AUROC over the strongest classical. Raw eval JSONs at `mamba-edge/artifacts/v0.2/baseline_*/` in the project repo. ### Is there a repository linking to use cases? - **https://github.com/ryypow/dendroaspis** ### What other tasks could this dataset be used for? - ATT&CK-technique-specific detector evaluation - Self-supervised pretraining for host-telemetry transformers - Lineage / process-tree representation learning - Temporal-anomaly-scoring baselines - Sequence-tokenization research (the `token` column is a structured composite identifier; alternative tokenizations can be derived) ### Is there anything about the dataset's composition that could result in unfair treatment? Single-host, single-developer baseline. Models trained on this corpus **will not generalize** to multi-host or multi-tenant environments without recalibration. The datacard's "Sources of bias" enumerates the specific limitations. ### Are there tasks for which the dataset should NOT be used? - **Production deployment.** Recalibration is mandatory; the hash-bucket sizes are right-sized for plausible multi-host saturation but observed utilization here is very low — see datacard's hash-bucket utilization table. - **Generalization claims about HIDS.** A 21-technique single-host evaluation is necessary but not sufficient evidence; use it as one bench among many, not the bench. - **Attack template / red-team training data.** The corpus is event-side telemetry, not payloads. --- ## 6. Distribution ### How will the dataset be distributed? Hugging Face Hub — `/dendroaspis-tetragon-hids` (URL filled in at publish time). The release bundle is a directory; the parquet shards are under `data/`, datacard / datasheet / license / citation at the root, and reproduction artifacts under `reproduction/`. SHA256 manifest included (`SHA256SUMS`) plus a JSON release manifest (`RELEASE.json`) recording the build timestamp + revision SHA + per-file sha256. ### Is there a fee? No. Public, free. ### When will the dataset be distributed? April 2026 ### Will the dataset be distributed under a copyright / IP license? CC BY 4.0 (data) + MIT (bundled scripts). ### Have any third parties imposed IP-based or other restrictions? No. ### Do any export controls / regulatory restrictions apply? No. Behavioral telemetry from a homelab; no controlled technologies are captured. --- ## 7. Maintenance ### Who is supporting / hosting / maintaining the dataset? Ryan William Powers via the Hugging Face Hub repo. Bug reports, errata, and corrections via GitHub issues on the project repository (linked from the datacard). ### How can the manager be contacted? GitHub: [@ryypow](https://github.com/ryypow). ### Will the dataset be updated? No. Future work includes similar experiments using telemetry derivede from other platforms. This will remain a stable citation target. ### If others want to contribute extensions / build on the dataset? Forking the dataset repo, applying changes, and citing the original is fine under CC BY 4.0. For substantive extensions (e.g. a multi-host re-collection), open a GitHub issue first to coordinate naming and avoid label-schema drift.