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AriaAICompany
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phish-structural

Text Classification
Joblib
English
Persian
scikit-learn
cybersecurity
phishing-detection
multilingual
Model card Files
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phish-structural / README.md
alirezaaminzadeh's picture
alirezaaminzadeh
Publish calibrated TF-IDF and LightGBM phishing reviewers
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metadata 
license: apache-2.0
library_name: scikit-learn
pipeline_tag: text-classification
language:
  - en
  - fa
tags:
  - cybersecurity
  - phishing-detection
  - text-classification
  - multilingual
datasets:
  - AriaAICompany/phish-messages

phish-structural

Small calibrated reviewers used by the Phish Space.

  • tfidf_lr.joblib — TfidfVectorizer(char_wb 3-5) + logistic regression, sigmoid-calibrated
  • lightgbm.joblib — LightGBM on structural features only, sigmoid-calibrated

Trained on the development campaigns of AriaAICompany/phish-messages (seed 3). Test campaigns are held out by campaign, sender group, and template.

Intended use

Decision support for a human reviewing a synthetic or already-shared message. Scores are not a safe/unsafe certificate and are not a probability of account compromise.

Out of scope

  • Fetching or rendering live URLs
  • Executing attachments
  • Judging malice from a logo screenshot
  • Treating copied SPF/DKIM/DMARC headers as a trusted receiver verdict
  • Production mailbox enforcement

Metrics

See metrics.json written by scripts/build_artifacts.py and evaluations/evaluation.json in the Space. Lab PR-AUC on synthetic fixtures is not operational accuracy.