README.md

---
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)