Real-time network anomaly detection system using machine learning for cybersecurity monitoring and threat detection

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

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.

Cybersecurity project implementing an Anomaly-Based Network Intrusion Detection System (IDS) using One-Class SVM and the NSL-KDD dataset for detecting network attacks, abnormal traffic, and malicious activities with Machine Learning.

Awesome Network Anomaly Detection

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

Analyzing US flight disruption data to identify operational risk patterns in aviation

A hybrid IDS for aircraft is proposed using Random Forests, Isolation Forests, YARA rules, and import hashing within a zero-trust architecture. Evaluated in a virtualized multi-zone testbed, it achieves high accuracy across six aviation datasets with low resource use, enabling practical onboard cybersecurity.

A context-aware intrusion detection framework extending Kitsune with adaptive risk assessment, explainable AI, and counterfactual explanations for network security.

A dashboard which tracks the alarms and alerts, statuses and metrics of the WLCG/OSG sites

AI-Powered Autonomous Network Defense Appliance for Real-Time Network Monitoring, Threat Detection, and Automated Response using Machine Learning.

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