| --- |
| 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<n<100K |
| configs: |
| - config_name: commands |
| data_files: |
| - split: "2021_2022" |
| path: "commands/2021_2022.jsonl" |
| - split: "2024" |
| path: "commands/2024.jsonl" |
| - config_name: sessions |
| data_files: |
| - split: "2021_2022" |
| path: "sessions/2021_2022.jsonl" |
| - split: "2024" |
| path: "sessions/2024.jsonl" |
| - config_name: request_response |
| default: true |
| data_files: |
| - split: "curated" |
| path: "request_response/curated.jsonl" |
| - split: "vm_replay_2021_2022" |
| path: "request_response/vm_replay_2021_2022.jsonl" |
| - config_name: attack_ttp_tactics_paper |
| data_files: |
| - split: "train" |
| path: "attack_ttp/paper_tactics.jsonl" |
| - config_name: attack_ttp_techniques_paper |
| data_files: |
| - split: "train" |
| path: "attack_ttp/paper_techniques.jsonl" |
| - config_name: attack_ttp_tactics_derived |
| data_files: |
| - split: "train" |
| path: "attack_ttp/derived_session_coverage_tactics.jsonl" |
| - config_name: attack_ttp_techniques_derived |
| data_files: |
| - split: "train" |
| path: "attack_ttp/derived_session_coverage_techniques.jsonl" |
| --- |
| |
| # Shell Honeypot Attack Request–Response Dataset |
|
|
| A standardized, MITRE ATT&CK–annotated dataset of **post-login shell |
| attacks** captured by [Cowrie](https://github.com/cowrie/cowrie) SSH/Telnet |
| honeypots across two collection periods — **2021–2022** and **2024**. It pairs |
| attacker shell commands with **real captured system responses**, enabling both |
| longitudinal threat analysis and the training/evaluation of AI-driven honeypots. |
|
|
| This is the open-source release accompanying the paper [*“Unveiling Evolving |
| Threats: A Data Analysis for Next-Generation Honeypot Development”*](https://ieeexplore.ieee.org/document/11360425/) |
| (IEEE SRDS 2025). |
|
|
| > 🔎 **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. |
|
|
