SalamaNet AI

Project Progress

3% Complete

We have successfully trained our ML model for phishing and anomaly detection. Currently integrating real-time network monitoring and alerting.

Current Challenges

Need financial support to fine-tune and deploy larger models on Azure cloud platforms.

Slow and unstable internet affecting real-time data collection and model retraining.

Limited access to high-quality phishing & anomaly datasets for robust training.

Balancing academic workload with development, testing, and deployment efforts.

Project Timeline

February 2025

SalamaNet AI project idea proposed and team formed during JHUB Africa workshop.
March 2025

Research phase began — exploring phishing detection techniques and network security gaps.
April 2025

Model training and dataset collection using open-source phishing & anomaly datasets.
May 2025

Integration of real-time alerts and early anomaly detection prototype demo.
June 2025

Website showcase developed and Azure cloud deployment planning started.
July 2025

Expected live deployment of SalamaNet AI on institutional network environments.

Project Stats

0

Model Accuracy (%)

0

Threats Detected

0

Phishing Attempts Blocked

Downloads & Demo

Explore our project resources and follow our development journey.

View Project Documents

Download Presentation

View on GitHub

Live Demo (Coming Soon)

About SalamaNet AI

Problem Statement

Increased cyber threats in public & education institutions due to poor cyber hygiene.
Phishing attacks targeting teachers, students, and government officials are becoming rampant.
There’s a lack of real-time detection and response in most Kenyan systems.

Our Solution

SalamaNet AI is a network-facing, AI-driven model that detects threats, anomalies, and phishing attacks in real time.
Our system uses ML to scan suspicious emails, sites, and attachments, sending instant alerts to administrators and automatically quarantining or blocking potential threats.