SCAMBERT: DistilBERT for SMS Fraud & Scam Detection
SCAMBERT is a fine-tuned distilbert-base-uncased model specifically designed to detect social engineering, financial fraud, phishing, and scam payloads in SMS and short-form conversational text. It is built as Layer 3 of the AI Honeypot (CIPHER) Threat Intelligence Pipeline.
Model Summary
- Model Type: Text Classification (Binary)
- Base Model:
distilbert-base-uncased
- Language: English (en)
- Task: Spam/Scam Detection
- License: MIT (or your project's license)
- Size: ~255 MB
Labels
0: Safe / Legitimate1: Scam / Fraud / Phishing
Performance & Metrics
The model was fine-tuned on a dataset of 8,438 samples (27.5% Scam / 72.5% Safe). Due to class imbalance, class weights were applied during training.
Calibration & Validation Results
- Best Accuracy: 99.41%
- Best F1-Score: 98.92%
- Calibrated Precision: 95.08%
- Calibrated Recall: 100.0%
- Optimal Threshold:
0.0028 (For high-recall environments)
Robustness Evaluation
The model was tested against common bad-actor obfuscation tactics:
| Tactic | Example Input | Prediction Probability | Passed |
|---|
| URL Obfuscation | Win $1000 fast! Click hxxp://scammy... | 99.9% Scam | ✅ |
| Numeric Substitution | W1NNER! Y0u have b33n select3d... | 99.3% Scam | ✅ |
| Mixed Case | cOnGrAtUlAtIoNs, yOu WoN a FrEe... | 89.8% Scam | ✅ |
Note: The model occasionally struggles with extremely short, contextless messages (e.g., "Call me now") as intended, relying on earlier heuristic layers for context.
Usage
You can use this model directly with Hugging Face's pipeline:
1from transformers import pipeline
2
3# Load the pipeline
4classifier = pipeline("text-classification", model="Digvijay05/SCAMBERT")
5
6# Inference
7text = "Earn Rs 5000 daily income from home part time. Click this link: http://bit.ly/job"
8result = classifier(text)
9
10print(result)
11# [{'label': 'LABEL_1', 'score': 0.99...}]
Or run via the Inference API:
1import httpx
2
3API_URL = "https://api-inference.huggingface.co/models/Digvijay05/SCAMBERT"
4headers = {"Authorization": "Bearer YOUR_HF_TOKEN"}
5
6response = httpx.post(API_URL, headers=headers, json={"inputs": "Your account is locked. Verify at bit.ly/secure"})
7print(response.json())
Deployment Considerations
- CPU Latency Estimate: ~10-30ms / sequence
- GPU Latency Estimate: ~2-5ms / sequence
- Recommendation: Can be efficiently hosted on serverless CPU environments (like Render Free Tier) using Hugging Face's Inference API, or deployed natively if 512MB+ RAM is available. ONNX quantization is recommended for edge deployments.
Intended Use
This model is designed as a semantic booster tie-breaker layer within a multi-layered classification engine. It excels at detecting complex sentence structures, urgency, and manipulative context that standard Regex/Heuristic rules might miss.
