An AI-Powered Cyber Threat Detection & Monitoring System for African Digital Infrastructure .
🔐 SalamaNET AI
AI-Powered Cybersecurity for Africa's Education & Public Sectors
SalamaNET AI is a smart, context-aware cybersecurity system built to detect threats, monitor anomalies, and prevent phishing attacks across public and educational institutions in Africa. Our goal is to enhance digital safety through real-time, automated, and locally-relevant AI solutions.
🚨 Problem Statement
"Increased cyberthreats due to poor cyber hygiene."
Educational and public sector systems in Africa — especially in countries like Kenya — are facing a surge in cyberattacks. Most institutions lack active monitoring tools, suffer from poor password practices, and are unaware of phishing tactics.
🌍 Why It Matters
Kenyan examples:
Public agency data breaches
Schools disrupted by ransomware
Leaked citizen data due to weak security practices
These events reflect a growing crisis that demands an affordable, intelligent, and homegrown solution.
🤖 The Solution: SalamaNET AI
SalamaNET AI is an AI-based platform designed to:
🛡️ Detect phishing attempts in real time
📡 Learn continuously from user feedback
✉️ Empower institutions with actionable visibility into threats
⚙️ How It Works (MVP / Phase 1)
Right now, the MVP (Minimum Viable Product) focuses on phishing detection and learning:
User enters message → Model predicts "safe" or "phishing" + confidence
↘ User feedback (correct/incorrect)
↘ Feedback logged & used for retraining
Key Features:
✅ Message classification (phishing/safe + confidence score)
✅ User feedback system (mark predictions as correct/incorrect)
✅ Retraining loop (model improves once enough new feedback is collected)
✅ Admin log viewer (daily summaries, filter by model version, view history)
📂 Project Structure
salamanet_ai/
│── app.py # Flask web app
│── retrain_if_needed.py # CLI helper for retraining
│── train_sklearn.py # Training script (TF-IDF + Logistic Regression)
│── predictions_log.csv # Saved predictions
│── feed …