auren-research/astraea-unified-threat

Astraea Unified Threat Dataset (v4)

Developed by Auren Research, Astraea Unified Threat (v4) is a production-balanced telemetry dataset for fine-tuning multi-task encoder architectures such as answerdotai/ModernBERT and chandar-lab/NeoBERT.

Each example is a structured security log or email payload with task-specific prefixes (for example, [TASK=LOG_CLS]), enabling a single model to perform log triage, IOC extraction, MITRE ATT&CK mapping, attack-chain classification, and phishing detection.

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Files

File	Rows	Size	Purpose
train.parquet	220,295	~84 MB	Primary training split
val.parquet	31,843	~14 MB	Validation for log classification and IOC NER
test.parquet	20,000	~22 MB	Held-out public phishing benchmark
astraea-unified-threat.parquet	270,263	~127 MB	Legacy unified export (optional)

Recommended for training: use train.parquet, val.parquet, and test.parquet. The unified file does not include all rows from the split release (2,971 additional training examples and a task-aware partition are only in the split files).

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Dataset Profile

Metric	Value	Notes
Total rows (splits)	272,138	train + val + test
Class balance (is_malicious)	44.2% malicious	Globally balanced across splits
Mean anomaly score	0.443	Continuous target in [0.0, 1.0]
Validated IOC spans	1,134,937	Character offsets verified on IOC rows
MITRE coverage	19 techniques, 10 tactics	On mitre_classification rows
Log types	21	Windows, Linux, cloud, EDR, network, IAM, K8s, WAF, email, and more
Sources	23	Synthetic telemetry + public phishing
Language	English	Standard telemetry and email syntax
Format	Parquet	Columnar, Hugging Face–compatible

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Train / Validation / Test Splits

Splits are task-aware and leakage-safe: no overlapping text values across train, val, or test.

train.parquet (220,295 rows)

Task prefix	label_type	Rows
[TASK=LOG_CLS]	log_classification	126,805
[TASK=IOC_NER]	ioc_extraction	48,231
[TASK=MITRE]	mitre_classification	24,650
[TASK=ATTACK_CHAIN]	attack_chain	12,000
[TASK=PHISH]	phishing_detection	8,609

Use for fine-tuning all supported tasks. Includes synthetic phishing (synthetic_email) plus adversarial and false-positive hard negatives.

val.parquet (31,843 rows)

Task prefix	label_type	Rows
[TASK=LOG_CLS]	log_classification	24,405
[TASK=IOC_NER]	ioc_extraction	7,438

Focused on network and supply-chain telemetry (netflow, CI/CD, HTTP, firewall, DNS). Malicious rate for log classification is ~19.1% (vs. ~4.3% in train), providing a harder validation signal than random sampling.

test.parquet (20,000 rows)

Task prefix	label_type	Rows
[TASK=PHISH]	phishing_detection	20,000

All rows are from public_phishing (Seven Phishing Email Datasets). Use this split for real-world phishing evaluation only.

Note: MITRE and attack-chain tasks currently have no dedicated test split. Hold out a subset of train.parquet or report metrics on validation data for those tasks.

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Supported Tasks

Task	Head type	Label columns	Eval split
Log triage	Sequence classification	is_malicious	val
IOC extraction	Token classification (NER)	ioc_spans	val
MITRE mapping	Multi-label classification	mitre_tactic, mitre_technique	train holdout
Attack chain	Sequence / multi-label classification	attack_stage	train holdout
Phishing detection	Sequence classification	is_malicious	test

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Technical Features

• 2026 threat-vector telemetry: Simulated enterprise attack surfaces including NHI/service-account abuse, CI/CD and OAuth compromise patterns, cloud audit anomalies, EDR alerts, and generative-AI-style spearphishing artifacts.
• Benign background baseline: Large volumes of benign Windows, Linux, database, cloud, and EDR telemetry to reduce false-positive saturation in production-like settings.
• Malicious augmentations: Additional malicious Windows, Linux, EDR, cloud, and netflow examples (not present in the legacy unified export) so models cannot rely on log-type shortcuts.
• Token boundary fuzzing: IPs, domains, hashes, and filenames are randomized across examples to encourage behavioral pattern learning over memorization.
• Public holdout: All 20,000 public phishing emails are isolated in test.parquet for unbiased evaluation.

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Data Schema

Each row contains 12 structured columns:

Column	Type	Description
row_id	int64	Row index within each split file (not a global ID across files)
text	string	Log stream or email payload; primary model input
source	string	Provenance label (e.g. synthetic_cloud, public_phishing)
label_type	string	Task router (log_classification, ioc_extraction, etc.)
mitre_tactic	string	MITRE ATT&CK tactic or none
mitre_technique	string	MITRE technique ID (e.g. T1059.001) or none
ioc_spans	JSON string	Span annotations: [{"start": int, "end": int, "type": str, "value": str}]
anomaly_score	float	Continuous score in [0.0, 1.0]
attack_stage	string	Kill-chain stage label(s)
is_malicious	bool	Binary threat label
log_type	string	Telemetry subtype (e.g. windows_event, kubernetes, iam)
metadata	JSON string	Ancillary provenance fields

IOC span types

ip, domain, email, filename, hash_md5, hash_sha256, hash_sha1, punycode, registry_key

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Quick Start

from datasets import load_dataset

# Load from a local directory or Hugging Face Hub
train = load_dataset("parquet", data_files="train.parquet", split="train")
val   = load_dataset("parquet", data_files="val.parquet", split="train")
test  = load_dataset("parquet", data_files="test.parquet", split="train")

# Example: log classification
logs = train.filter(lambda x: x["label_type"] == "log_classification")
print(logs[0]["text"])
print(logs[0]["is_malicious"])

For multi-task fine-tuning, pass the full text field (including the [TASK=...] prefix) to models such as ModernBERT, and use task-specific classification or token heads keyed on label_type.

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Citation

@misc{astraea-unified-threat-v4,
  title        = {Astraea Unified Threat Dataset v4: Balanced Long-Context Telemetry for Multi-Task SecOps Encoders},
  author       = {Auren Research},
  year         = {2026},
  publisher    = {Hugging Face},
  howpublished = {\url{https://huggingface.co/auren-research/astraea-unified-threat}}
}