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 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