Explore Network Anomaly Detection Project 📊💻. It achieves an exceptional 99.7% accuracy through a blend of supervised and unsupervised learning, extensive feature selection, and model experimentation. Stunning data visualizations using synthetic network traffic data offer insightful representations of anomalies, enhancing network security.

🧱 [🏅TALENT LAND HACKATHON FINALIST] Desktop system with Artificial Intelligence to detect cybersecurity attacks in network; also considering the prevention of phishing and scam.

An attempt at the network anomaly detection task using manually implemented k-means, spectral clustering and DBSCAN algorithms, with manually implemented evaluation metrics (precision, recall, f1-score and conditional entropy) used to evaluate these algorithms.

10 Latest Final Year Project Includes Source Code, PPT, Synopsis, Report, Documents, Base Research Paper & Video tutorials

Project designed to identify unusual patterns or activities in network traffic that could indicate potential security threats, such as attacks, intrusions, or breaches. Network Anomaly Detection System Using Machine Learning With Includes Source Code, PPT, Synopsis, Report, Documents, Base Research Paper & Video tutorials

This project compares between different clustering algorithms: K-Means, Normalized Cut and DBSCAN algorithms for network anomaly detection on the KDD Cup 1999 dataset