Datasets:

solsticestudioai
/

nemesis-cyber-pack

@@ -1,156 +1,155 @@
----
-license: cc-by-4.0
-task_categories:
-  - tabular-classification
-  - text-classification
-language:
-  - en
-tags:
-  - synthetic
-  - cybersecurity
-  - threat-intelligence
-  - red-team
-  - blue-team
-  - soc
-  - siem
-  - edr
-  - mitre-attack
-  - detection-engineering
-  - security-analytics
-  - adversarial-simulation
-  - agentic-ai
-pretty_name: Nemesis Cyber Threat Simulation Pack
-size_categories:
-  - 10K<n<100K
-configs:
-  - config_name: default
-    data_files:
-      - split: train
-        path: nemesis_cyber_sample.parquet
----
-# Nemesis Cyber Threat Simulation Pack (Sample)
-**A synthetic adversarial-agent cyber operations dataset for detection-model training, SOC analyst triage research, and blue-team evaluation.** Each row captures a complete simulated attack episode: triggering anomaly, environment context, adversarial planner reasoning, correlated telemetry trace, execution summary, and final decision outcome (detected / blocked / impact achieved / stealth maintained / exfiltration complete).
-Built by [SolsticeAI](https://solsticestudio.ai) as a free sample of a larger commercial pack. 100% synthetic. No real incident, victim, or exploit data — and no working offensive code. TTP labels align with MITRE ATT&CK vocabulary so this sample can be used to train and benchmark defenders.
-## What is included
-| File | Rows | Format | Purpose |
-|---|---:|---|---|
-| `nemesis_cyber_sample.parquet` | 10,000 | Parquet | Columnar, typed, best for analytics |
-| `nemesis_cyber_sample.jsonl` | 10,000 | JSON Lines | Streaming / LLM training friendly |
-**Source pack:** 2.5M-episode corpus
-**This sample:** 10,000 episodes, stratified 2,000 per outcome class
-**Outcome classes:** `detected_by_soc`, `blocked_by_edr`, `stealth_maintained`, `exfiltration_complete`, `impact_achieved`
-**Environments covered:** AWS-Cloud, Active-Directory, Kubernetes, Web-App-Gateway
-## Record structure
-Each record is one simulated attack episode with 8 top-level fields:
-| Field | Type | Contents |
-|---|---|---|
-| `schema_version` | string | Pack schema version (`1.0.0-nemesis-cyber-sample`) |
-| `event` | struct | `id`, `timestamp`, `trace_id`, `weighted_score`, `decision_outcome` |
-| `risk_context` | struct | `trigger`, `protocol`, `chain`, `impacted_asset`, `anomaly_signature` |
-| `agent_reasoning` | struct | `engine`, `winning_strategy`, `confidence_score`, `mcts_branches` |
-| `correlated_telemetry` | list<struct> | Ordered action chain with per-step telemetry (latency, noise, evasion score, node provider) |
-| `execution_summary` | struct | `strategy`, `success_rate`, `total_execution_ms`, `noise_penalty` |
-| `genetic_optimizer_feedback` | struct | `fitness_score_update`, `parameter_drift` |
-| `decision_outcome` | string | Final label (duplicated from `event.decision_outcome` for convenience) |
-See [SCHEMA.md](./SCHEMA.md) for the full nested field breakdown.
-## Why this dataset is useful
-Most public cybersecurity datasets are either raw packet captures, static CTI feeds, or narrow single-technique labeling sets. This pack is shaped around what detection-engineering and SOC-analytics teams actually need to train modern models:
-- Multi-step attack episodes rather than isolated alerts
-- Balanced outcome classes across detected, blocked, stealthy, and successful attempts
-- Adversarial reasoning trace (strategy + MCTS branch count + confidence) alongside the telemetry
-- Per-step evasion and noise signals to train detection models that weigh stealth vs noise trade-offs
-- Cross-environment coverage (cloud, identity, container, web)
-- Stable schema suitable for dashboard prototyping, triage simulators, and ML pipelines
-## Typical use cases
-- SOC triage and alert-prioritization model training
-- Detection engineering rule evaluation against balanced positive and negative cases
-- Adversarial-AI research on multi-step planner behavior
-- Tabletop and red-vs-blue simulator content
-- LLM fine-tuning on incident narratives and defender reasoning
-- Benchmarking anomaly-scoring and false-positive reduction pipelines
-- Dashboard and BI template development for security analytics
-## Quick start
-```python
-import pandas as pd
-import pyarrow.parquet as pq
-df = pq.read_table("nemesis_cyber_sample.parquet").to_pandas()
-# Outcome distribution (stratified balanced)
-print(df["decision_outcome"].value_counts())
-# Evasion pressure per environment
-df["protocol"] = df["risk_context"].apply(lambda r: r.get("protocol"))
-df["avg_evasion"] = df["correlated_telemetry"].apply(
-    lambda steps: sum(s["telemetry"]["evasion_score"] for s in steps) / max(len(steps), 1)
-)
-print(df.groupby("protocol")["avg_evasion"].mean().round(3))
-# Detection-rate by trigger type
-df["trigger"] = df["risk_context"].apply(lambda r: r.get("trigger"))
-detection_rate = (df["decision_outcome"].isin(["detected_by_soc", "blocked_by_edr"])
-                  .groupby(df["trigger"]).mean().round(3))
-print(detection_rate)
-```
-Streaming form:
-```python
-import json
-with open("nemesis_cyber_sample.jsonl") as f:
-    for line in f:
-        episode = json.loads(line)
-        # one episode per line
-```
-## Responsible use
-This dataset is intended for **defensive** research: detection modeling, SOC tooling, and adversarial-agent studies. It contains synthesized attack metadata and MITRE-aligned TTP labels — it does **not** contain working offensive payloads, exploit code, shellcode, malware samples, credentials, private vulnerability details, or any real-world victim data. Please use it to improve defenses.
-## License
-Released under **CC BY 4.0**. Use freely for research, detection-engineering, education, and commercial prototyping with attribution.
-## Get the full pack
-This Hugging Face repo is a **10K-episode sample**. The production pack scales to 2.5M+ episodes, additional outcome labels, richer per-step telemetry, attacker/defender variant splits, multi-environment campaign chains, parquet + JSONL + SIEM-import formats, and buyer-specific variants.
-**Self-serve (Stripe checkout):**
-- [**Sample Scale tier — $5,000**](https://buy.stripe.com/7sY5kD2j85QTfSb5lfeEo03) — ~25K records, one subject, 72-hour delivery.
-**Full pack + enterprise scope:**
-- [solsticestudio.ai/datasets](https://solsticestudio.ai/datasets) — per-SKU pricing across Starter / Professional / Enterprise tiers.
-- [solsticestudio.ai/contact](https://solsticestudio.ai/contact) — discovery call for commercial licensing, custom generation, or buyer-specific variants.
-**Procurement catalog:**
-- [SolsticeAI Data Storefront](https://solsticeai.mydatastorefront.com) — available via Datarade / Monda.
-## Citation
-```bibtex
-@dataset{solstice_nemesis_cyber_pack_2026,
-  title        = {Nemesis Cyber Threat Simulation Pack (Sample)},
-  author       = {SolsticeAI},
-  year         = {2026},
-  publisher    = {Hugging Face},
-  url          = {https://huggingface.co/datasets/solsticestudioai/nemesis-cyber-pack}
-}
-```

+---
+license: cc-by-4.0
+task_categories:
+  - tabular-classification
+  - text-classification
+language:
+  - en
+tags:
+  - synthetic
+  - cybersecurity
+  - threat-intelligence
+  - red-team
+  - blue-team
+  - soc
+  - siem
+  - edr
+  - mitre-attack
+  - detection-engineering
+  - security-analytics
+  - adversarial-simulation
+  - agentic-ai
+pretty_name: Nemesis Cyber Threat Simulation Pack
+size_categories:
+  - 10K<n<100K
+configs:
+  - config_name: default
+    data_files:
+      - split: train
+        path: nemesis_cyber_sample.parquet
+---
+# Nemesis Cyber Threat Simulation Pack (Sample)
+**A synthetic adversarial-agent cyber operations dataset for detection-model training, SOC analyst triage research, and blue-team evaluation.** Each row captures a complete simulated attack episode: triggering anomaly, environment context, adversarial planner reasoning, correlated telemetry trace, execution summary, and final decision outcome (detected / blocked / impact achieved / stealth maintained / exfiltration complete).
+Built by [SolsticeAI](https://www.solsticestudio.ai/datasets) as a free sample of a larger commercial pack. 100% synthetic. No real incident, victim, or exploit data — and no working offensive code. TTP labels align with MITRE ATT&CK vocabulary so this sample can be used to train and benchmark defenders.
+## What is included
+| File | Rows | Format | Purpose |
+|---|---:|---|---|
+| `nemesis_cyber_sample.parquet` | 10,000 | Parquet | Columnar, typed, best for analytics |
+| `nemesis_cyber_sample.jsonl` | 10,000 | JSON Lines | Streaming / LLM training friendly |
+**Source pack:** 2.5M-episode corpus
+**This sample:** 10,000 episodes, stratified 2,000 per outcome class
+**Outcome classes:** `detected_by_soc`, `blocked_by_edr`, `stealth_maintained`, `exfiltration_complete`, `impact_achieved`
+**Environments covered:** AWS-Cloud, Active-Directory, Kubernetes, Web-App-Gateway
+## Record structure
+Each record is one simulated attack episode with 8 top-level fields:
+| Field | Type | Contents |
+|---|---|---|
+| `schema_version` | string | Pack schema version (`1.0.0-nemesis-cyber-sample`) |
+| `event` | struct | `id`, `timestamp`, `trace_id`, `weighted_score`, `decision_outcome` |
+| `risk_context` | struct | `trigger`, `protocol`, `chain`, `impacted_asset`, `anomaly_signature` |
+| `agent_reasoning` | struct | `engine`, `winning_strategy`, `confidence_score`, `mcts_branches` |
+| `correlated_telemetry` | list<struct> | Ordered action chain with per-step telemetry (latency, noise, evasion score, node provider) |
+| `execution_summary` | struct | `strategy`, `success_rate`, `total_execution_ms`, `noise_penalty` |
+| `genetic_optimizer_feedback` | struct | `fitness_score_update`, `parameter_drift` |
+| `decision_outcome` | string | Final label (duplicated from `event.decision_outcome` for convenience) |
+See [SCHEMA.md](./SCHEMA.md) for the full nested field breakdown.
+## Why this dataset is useful
+Most public cybersecurity datasets are either raw packet captures, static CTI feeds, or narrow single-technique labeling sets. This pack is shaped around what detection-engineering and SOC-analytics teams actually need to train modern models:
+- Multi-step attack episodes rather than isolated alerts
+- Balanced outcome classes across detected, blocked, stealthy, and successful attempts
+- Adversarial reasoning trace (strategy + MCTS branch count + confidence) alongside the telemetry
+- Per-step evasion and noise signals to train detection models that weigh stealth vs noise trade-offs
+- Cross-environment coverage (cloud, identity, container, web)
+- Stable schema suitable for dashboard prototyping, triage simulators, and ML pipelines
+## Typical use cases
+- SOC triage and alert-prioritization model training
+- Detection engineering rule evaluation against balanced positive and negative cases
+- Adversarial-AI research on multi-step planner behavior
+- Tabletop and red-vs-blue simulator content
+- LLM fine-tuning on incident narratives and defender reasoning
+- Benchmarking anomaly-scoring and false-positive reduction pipelines
+- Dashboard and BI template development for security analytics
+## Quick start
+```python
+import pandas as pd
+import pyarrow.parquet as pq
+df = pq.read_table("nemesis_cyber_sample.parquet").to_pandas()
+# Outcome distribution (stratified balanced)
+print(df["decision_outcome"].value_counts())
+# Evasion pressure per environment
+df["protocol"] = df["risk_context"].apply(lambda r: r.get("protocol"))
+df["avg_evasion"] = df["correlated_telemetry"].apply(
+    lambda steps: sum(s["telemetry"]["evasion_score"] for s in steps) / max(len(steps), 1)
+)
+print(df.groupby("protocol")["avg_evasion"].mean().round(3))
+# Detection-rate by trigger type
+df["trigger"] = df["risk_context"].apply(lambda r: r.get("trigger"))
+detection_rate = (df["decision_outcome"].isin(["detected_by_soc", "blocked_by_edr"])
+                  .groupby(df["trigger"]).mean().round(3))
+print(detection_rate)
+```
+Streaming form:
+```python
+import json
+with open("nemesis_cyber_sample.jsonl") as f:
+    for line in f:
+        episode = json.loads(line)
+        # one episode per line
+```
+## Responsible use
+This dataset is intended for **defensive** research: detection modeling, SOC tooling, and adversarial-agent studies. It contains synthesized attack metadata and MITRE-aligned TTP labels — it does **not** contain working offensive payloads, exploit code, shellcode, malware samples, credentials, private vulnerability details, or any real-world victim data. Please use it to improve defenses.
+## License
+Released under **CC BY 4.0**. Use freely for research, detection-engineering, education, and commercial prototyping with attribution.
+## Get the full pack
+This Hugging Face repo is a **10K-episode sample**. The production pack scales to 2.5M+ episodes, additional outcome labels, richer per-step telemetry, attacker/defender variant splits, multi-environment campaign chains, parquet + JSONL + SIEM-import formats, and buyer-specific variants.
+**Self-serve (Stripe checkout):**
+- [**Sample Scale tier — $5,000**](https://buy.stripe.com/7sY5kD2j85QTfSb5lfeEo03) — ~25K records, one subject, 72-hour delivery.
+**Full pack + enterprise scope:**
+- [www.solsticestudio.ai/datasets](https://www.solsticestudio.ai/datasets) — per-SKU pricing across Starter / Professional / Enterprise tiers, plus commercial licensing, custom generation, and buyer-specific variants.
+**Procurement catalog:**
+- [SolsticeAI Data Storefront](https://solsticeai.mydatastorefront.com) — available via Datarade / Monda.
+## Citation
+```bibtex
+@dataset{solstice_nemesis_cyber_pack_2026,
+  title        = {Nemesis Cyber Threat Simulation Pack (Sample)},
+  author       = {SolsticeAI},
+  year         = {2026},
+  publisher    = {Hugging Face},
+  url          = {https://huggingface.co/datasets/solsticestudioai/nemesis-cyber-pack}
+}
+```