| --- |
| license: cc-by-4.0 |
| task_categories: |
| - tabular-classification |
| - text-classification |
| language: |
| - en |
| tags: |
| - synthetic |
| - cybersecurity |
| - edr |
| - soc |
| - malware |
| - intrusion-detection |
| - adversarial-ml |
| pretty_name: Solstice Nemesis Cyber Adversarial Traces |
| size_categories: |
| - n<1K |
| configs: |
| - config_name: traces |
| data_files: |
| - split: train |
| path: data/traces/train.parquet |
| --- |
| |
| # Solstice Nemesis Cyber Adversarial Traces |
|
|
| **High-fidelity synthetic cybersecurity event traces for SOC and EDR model training.** This public sample contains 500 synthetic security trace rows simulating adversarial behavior, Endpoint Detection and Response (EDR) telemetry, and automated block outcomes. |
|
|
| Built by [Solstice AI Studio](https://www.solsticestudio.ai/datasets) as a free sample of a larger commercial pack. 100% synthetic — no real network logs or proprietary exploit code. |
|
|
| ## What makes this different |
| Most public cybersecurity datasets are static snapshots of IP traffic. **Nemesis** focuses on the **decision logic** of both the attacker and the defender. Each trace includes granular telemetry that would be seen by a modern EDR agent, including process execution, memory modification attempts, and behavioral blocking triggers. |
|
|
| ## What's in the box |
| - **Decision Traces (`traces`):** 500 deep-dive traces of adversarial sequences that were blocked by simulated EDR logic. |
|
|
| ## Use Cases |
| - **EDR Rule Benchmarking:** Test the efficacy of detection rules against high-fidelity synthetic anomalies. |
| - **Cybersecurity LLM Training:** Fine-tune models to interpret security logs and explain "why" a specific process sequence was flagged as malicious. |
| - **SOC Analyst Training:** Populate training environments with realistic alert sequences. |
|
|
| ## Data Provenance |
| Generated using Solstice’s PhantasOS / SIMA simulation engine. The simulation models attacker "personas" with varying skill levels attempting to move laterally, escalate privileges, and exfiltrate data while navigating a simulated corporate environment. |
|
|
| ## Get the Full Pack |
| Scale this dataset to 2.5M+ or 100M+ events, custom network topologies, and latest CVE-matched behavioral patterns. |
| [www.solsticestudio.ai/datasets](https://www.solsticestudio.ai/datasets) |
|
|
