xanderabim/fraudfoxai-phishing

FraudFoxAI Phishing Detection Model

Fine-tuned DistilBERT model for detecting phishing and fraudulent emails. Trained on 565,000+ curated emails with 99.71% accuracy.

Model Details

• —Base Model: distilbert-base-uncased
• —Training Data: 565,293 curated emails from multiple sources
• —Inference Runtime: ONNX Runtime (PyTorch + ONNX available)
• —Classes:
• —LABEL_0: Legitimate Email
• —LABEL_1: Phishing/Fraud Email

Performance

Training Data

Trained on 565,293 curated emails from multiple sources:

• —Corporate email archives (legitimate emails)
• —Reported phishing samples
• —Known 419/advance-fee fraud emails
• —Community-sourced spam and scam samples

Continuously improved with user feedback.

Training Configuration

• —Epochs: 2
• —Batch Size: 32
• —Warmup Steps: 1,000
• —Weight Decay: 0.01
• —Max Length: 512 tokens
• —Framework: PyTorch + Transformers
• —Training Time: ~12 hours on Colab GPU

Usage

ONNX Runtime (recommended, low memory)

from optimum.onnxruntime import ORTModelForSequenceClassification
from transformers import AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("xanderabim/fraudfoxai-phishing")
model = ORTModelForSequenceClassification.from_pretrained("xanderabim/fraudfoxai-phishing")

inputs = tokenizer("URGENT: Verify your account now!", return_tensors="np", truncation=True)
outputs = model(**inputs)

PyTorch

from transformers import AutoTokenizer, AutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("xanderabim/fraudfoxai-phishing")
model = AutoModelForSequenceClassification.from_pretrained("xanderabim/fraudfoxai-phishing")

inputs = tokenizer("URGENT: Verify your account now!", return_tensors="pt", truncation=True)
outputs = model(**inputs)

Sample Predictions

Production API

Deployed at: https://fraudfoxai.xanderabim.workers.dev

Or forward any email to: check@fraudfox.ai

Limitations

• —English language only
• —Max 512 tokens per input
• —May flag aggressive marketing emails as phishing
• —Subject-only inputs are less accurate than full email (subject + body)

License

MIT

Author

@xanderabim