--- license: cc-by-4.0 language: - en pretty_name: Shell Honeypot Attack Request–Response Dataset tags: - cybersecurity - honeypot - cowrie - shell - mitre-attack - intrusion-detection - threat-intelligence task_categories: - text-generation - text-classification size_categories: - 10K 🔎 **Dataset Viewer.** Use the interactive table at the top of this page to > browse the data without downloading anything: pick a **config** from the > dropdown (`request_response` *(default)*, `commands`, `sessions`, > `attack_ttp_tactics_paper`, `attack_ttp_techniques_paper`, > `attack_ttp_tactics_derived`, `attack_ttp_techniques_derived`) and a **split**, > then page or run SQL over the rows. The flagship `request_response` / > `curated` split is the quickest way to see command → real-response pairs with > their `system_change` and `severity_vi` labels. ## Why this dataset Honeypot research has long been held back by the absence of an open, high-fidelity **request–response** dataset. Most public corpora contain isolated attack logs with no system responses, making it impossible to benchmark how convincingly a honeypot *replies* to an attacker. This dataset addresses that gap by providing: - **Real system responses** for attacker commands (captured by replaying attacks inside instrumented production-like environments), not just the requests. - **A longitudinal pair of periods** (2021–2022 vs. 2024) with comparable volume, enabling study of how shell-attack tactics evolve. - **Per-session MITRE ATT&CK technique sequences**, so each interaction is mapped to adversary tactics and techniques. - **A harm/severity index `Vi ∈ [0,4]`** on the curated request–response split, for training response-risk classifiers. ## Dataset structure The dataset is published as loadable JSONL configs. `request_response` is the **default** config. | Config | Splits | Rows | One row = | |---|---|---:|---| | `request_response` *(default)* | `vm_replay_2021_2022`, `curated` | 1,512 / 1,489 | a single `command → real system response` turn | | `commands` | `2021_2022`, `2024` | 12,923 / 4,236 | a unique shell command + its frequency, complexity, and abstracted pattern | | `sessions` | `2021_2022`, `2024` | 5,365 / 6,658 | one effective attack session (commands grouped by attacker IP) + its ATT&CK technique sequence | | `attack_ttp_tactics_paper` | `train` | 9 | SRDS Table VI — ATT&CK tactic usage share per period (authoritative) | | `attack_ttp_techniques_paper` | `train` | 26 | SRDS Table VII — ATT&CK technique usage share per period (authoritative) | | `attack_ttp_tactics_derived` | `train` | 19 | per-session tactic coverage recomputed from the released sessions (supplementary) | | `attack_ttp_techniques_derived` | `train` | 49 | per-session technique coverage recomputed from the released sessions (supplementary) | ```python from datasets import load_dataset # Flagship request–response interactions (real captured responses) rr = load_dataset("Ziyang23423432/shell-attack-evolution-dataset", "request_response", split="curated") print(rr[0]) # Per-period sessions with ATT&CK technique sequences sess = load_dataset("Ziyang23423432/shell-attack-evolution-dataset", "sessions", split="2024") ``` ### `commands` Each unique command observed in a period. | Field | Type | Description | |---|---|---| | `command` | string | the raw shell command as typed by the attacker | | `period` | string | `2021_2022` or `2024` | | `frequency` | int | number of times this exact command was observed | | `is_complex` | bool | true if the command contains shell operators (`\| & ; > < \`` `$(`) | | `command_pattern` | string \| null | the abstracted operator pattern (parameters stripped) | ### `sessions` Each effective attack session = all post-authentication commands from one attacker IP, in order. | Field | Type | Description | |---|---|---| | `session_id` | string | stable anonymized id, `sha1(period\|ip)[:12]` | | `period` | string | `2021_2022` or `2024` | | `src_ip` | string | attacker source IP (see *Ethics* below) | | `commands` | list[string] | ordered commands issued in the session | | `command_count` | int | session length (number of commands) | | `session_pattern` | string \| null | the session's command-pattern signature | | `attack_techniques` | list[string] | ordered MITRE ATT&CK techniques invoked (consecutive duplicates collapsed) | ### `request_response` *(flagship)* A single attacker turn paired with the **real** system response. | Field | Type | Description | |---|---|---| | `session_id` | string | session this turn belongs to | | `period` | string | origin period of the attack commands | | `turn_index` | int | 0-based position of the turn within the session | | `command` | string | the attacker request | | `response` | string | the real terminal/system response | | `system_change` | string \| null | natural-language description of the induced system state change (curated split only) | | `severity_vi` | int \| null | harm index `Vi ∈ [0,4]`, higher = more dangerous (curated split only) | | `response_source` | string | `real_vm` or `curated_ubuntu` (see below) | Two response variants are provided **for the same set of representative attack-session templates** (the split key sets are identical), which makes this a natural *real-vs-curated* response benchmark: - **`vm_replay_2021_2022`** — raw responses captured by replaying the attacks on a real VM (`real_vm`). Locale strings may appear in the original (non-English) system language; preserved verbatim for fidelity. - **`curated`** — the same sessions replayed in a standardized **Ubuntu 22.04** environment (`curated_ubuntu`), with clean English output and two added annotations: a `system_change` description and a `severity_vi` harm index. `severity_vi` distribution on the curated split: `0`: 591, `1`: 35, `2`: 669, `3`: 71, `4`: 123 (+ a few unscored turns). ### `attack_ttp_*` (ATT&CK usage frequencies) MITRE ATT&CK usage frequencies, in two views split across four single-table configs (each a single `train` split): - **Authoritative (the paper's figures), shipped verbatim** — `attack_ttp_tactics_paper` (SRDS Table VI) and `attack_ttp_techniques_paper` (Table VII): columns `share_2021_2022`, `share_2024`, `trend` — command-weighted shares exactly as reported in the paper (e.g. Defense Evasion `0.0758 → 0.3877`). - **Supplementary, derived from the released sessions** — `attack_ttp_tactics_derived` / `attack_ttp_techniques_derived`: a per-session *coverage* share (`session_count / total_sessions_in_period`) recomputed from this dataset. It uses a different denominator than the paper's command-weighted share, so values differ; provided for reproducibility, not as the headline figure. ```python from datasets import load_dataset tactics = load_dataset("Ziyang23423432/shell-attack-evolution-dataset", "attack_ttp_tactics_paper", split="train") ``` ## Data collection & preprocessing Attacks were captured by Cowrie honeypots deployed on the public Internet (2021–2022 sources `cowrie-20210406–20210611` and `cowrie-20220609–20220704`; 2024 captured over 2024-03-01 – 2024-06-26). Only sessions that successfully authenticated and issued commands are retained. Sessions are segmented per attacker IP. The raw logs were cleaned for three Cowrie-specific artifacts: (1) incorrectly segmented multi-line shell scripts, (2) redundant echo/prompt lines wrongly recorded as commands, and (3) repeated automated re-attacks from the same source. System responses were collected by **replaying** grouped sessions in production-like environments; live malware download URLs were replaced with inert local files so that responses could be captured safely. See [`../analysis/`](../analysis) for the full, runnable preprocessing pipeline and [`../scripts/build_dataset.py`](../scripts/build_dataset.py) for the exact code that derives every file here. `raw_samples/cowrie_ssh_2024_sample.jsonl` contains a 300-line sample of the raw Cowrie event log format for reference. ## Intended uses - **AI-driven honeypot training** — teach a model to generate realistic shell responses from `request_response`. - **Response-risk classification** — predict `severity_vi` from a command/response. - **TTP / threat-intelligence analysis** — study tactic evolution via `sessions` and `attack_ttp`. - **Honeypot fidelity benchmarking** — compare a candidate honeypot's responses against the real `vm_replay` / `curated` responses. ## Ethics, safety & limitations - **No malware is distributed.** Binary payloads downloaded during the original captures are deliberately excluded; only defanged download URLs / filenames are retained for analysis. - **Attacker IPs** are included (as is standard for honeypot corpora) because they carry research value; they belong to attacking hosts, not victims. A stable anonymized `session_id` is also provided if you prefer to drop `src_ip`. - Commands may contain credentials/keys that **attackers** typed; these are the attackers' own injected values, not third-party secrets. - Honeypot data reflects only a subset of global attack activity and is biased by deployment geography and the honeypot's emulated profile. Curated responses are produced in one standardized environment and may differ from other systems. - **Antivirus note (Windows).** Because the `request_response` / `commands` fields contain *real* attacker payloads (encoded shellcode, malware download commands, etc.), local antivirus — notably Windows Defender — may quarantine the cache files `datasets` writes while building the dataset (e.g. `WinError 225`). This is an expected false positive on real threat data, not malware in the loader. Loading on Linux (incl. the Hugging Face Hub viewer/infra) is unaffected; on Windows, add an exclusion for the `~/.cache/huggingface` folder or load with `keep_in_memory=True`. ## Citation ```bibtex @inproceedings{wang2025unveiling, title = {Unveiling Evolving Threats: A Data Analysis for Next-Generation Honeypot Development}, author = {Wang, Ziyang and Lv, Shichao and Wang, Haining and You, Jianzhou and Liu, Shuoyang and Yuan, Tianwei and Lu, Xiao and Sun, Limin}, booktitle = {IEEE International Symposium on Reliable Distributed Systems (SRDS)}, year = {2025}, url = {https://ieeexplore.ieee.org/document/11360425/} } ``` ## License Released under **CC BY 4.0**. The accompanying code in this repository is under the MIT License.