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 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	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 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

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:

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 — ~25K records, one subject, 72-hour delivery.

Full pack + enterprise scope:

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 — available via Datarade / Monda.

Citation

@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}
}