ErenAta00/turkish-phishsoc-50k

Turkish PhishSOC-50K

Turkish PhishSOC-50K is a synthetic Turkish phishing and SOC-reasoning dataset for defensive email-security research. It is designed for models that classify emails, identify observable security indicators, explain the decision, and recommend a SOC action.

All examples are defanged. Domains use .example.invalid; the dataset contains no real people, organizations, live URLs, real attachments, operational phishing infrastructure, or real OSINT.

Summary

Field	Value
Public master examples	46,310
Train	42,560
Validation	3,750
Train label balance	21,268 benign / 21,292 malicious
Language	Turkish
Data type	Synthetic, defanged
Exports	Master + SFT + DPO train/validation

Purpose

This dataset is built for Turkish defensive cybersecurity workflows where a model should do more than output benign or malicious.

It supports:

• phishing and benign email classification,
• SOC-style rationale generation,
• observable indicator extraction,
• recommended-action prediction,
• supervised fine-tuning,
• DPO/preference optimization,
• shortcut-learning and hard-negative evaluation.

Files

data/
  master_train.jsonl
  master_validation.jsonl
  sft_train_messages.jsonl
  sft_validation_messages.jsonl
  dpo_train.jsonl
  dpo_validation.jsonl

README.md

Format

The master records include:

• label
• attack_vector
• risk_level
• cognitive_vector
• context_input
• input_text
• email_surface
• soc_answer
• quality_annotations

The SFT export uses chat-style messages. The DPO export uses prompt, chosen, and rejected preference pairs aligned to the same underlying examples.

Fine-Tuning Readiness

The dataset can be used directly with JSON-aware loaders such as datasets.load_dataset("json", ...).

• For SFT, use data/sft_train_messages.jsonl and data/sft_validation_messages.jsonl.
• For DPO, use data/dpo_train.jsonl and data/dpo_validation.jsonl.
• The escaped quotes and newline markers shown in the Hugging Face Data Viewer are normal JSON string rendering, not a training issue.
• The assistant targets are structured JSON strings by design, so the model learns to return parseable SOC decisions.

Quick Load

from datasets import load_dataset

dataset = load_dataset(
    "json",
    data_files={
        "train": "data/master_train.jsonl",
        "validation": "data/master_validation.jsonl",
    },
)

Quality Control

The release passed local checks for:

• split integrity and leakage,
• annotation consistency,
• shortcut-heavy feature balance,
• lexical diversity,
• DPO rejected-answer calibration,
• hard-negative coverage,
• defanged safety constraints.

Final deep QC reports zero blocker, major, and minor issues. One non-blocking watch note remains: two large benign vectors, Legitimate-HR and Legitimate-IT, have higher intra-vector lexical similarity than the rest of the set.

Intended Use

Allowed:

• defensive phishing detection research,
• Turkish SOC-assistant training,
• SFT/LoRA and DPO experiments,
• controlled academic benchmarking.

Not allowed:

• phishing campaign generation,
• credential harvesting,
• targeting real people or organizations,
• live-domain phishing simulation,
• offensive social-engineering automation.

Limitations

This is a synthetic dataset. It should not be used as proof of real inbox performance by itself. Real-world transfer should be measured with a separate anonymized out-of-distribution benchmark.

License

Released under CC BY-NC 4.0.

Citation

@misc{turkish_phishsoc_50k_2026,
  title = {Turkish PhishSOC-50K: A Synthetic Turkish Phishing and SOC Reasoning Dataset},
  year = {2026},
  language = {Turkish},
  note = {Defanged synthetic defensive cybersecurity dataset with master, SFT, and DPO exports}
}