metadata
license: mit
tags:
- cybersecurity
- africa
- threat-detection
- NLP
- Allsafeafrica
- cyber-aware
datasets:
- HuggingFaceFW/fineweb-2
metrics:
- accuracy
- bertscore
base_model:
- HuggingFaceTB/SmolLM3-3B
- google/gemma-3n-E4B-it
new_version: HuggingFaceTB/SmolLM3-3B
pipeline_tag: text-classification
library_name: adapter-transformers
🛡️ Cyber Threat Detector Africa
Developed by Allsafeafrica
A lightweight NLP model built to detect and classify potential cybersecurity threats in textual data across African SMEs, startups, and digital communities.
📌 Overview
Cyber Threat Detector Africa is an AI-powered model designed to:
- Classify cyber risk indicators in natural language (emails, messages, reports)
- Support awareness in employee training platforms
- Act as a backend tool for ESG-cyber hybrid security assessments
🧠 Model Info
| Attribute | Detail |
|---|---|
| Framework | transformers, pytorch |
| Base Model | distilbert-base-uncased |
| Fine-tuned On | Synthetic + local African threat incident data |
| Labels | phishing, malware, social-engineering, safe, suspicious |
| Accuracy | ~91.7% on test set |
✨ Example Usage
from transformers import pipeline
threat_detector = pipeline("text-classification", model="allsafeafrica/cyber-threat-detector-africa")
text = "Your account has been suspended. Click here to verify your identity."
threat_detector(text)