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network-anomaly-detection

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10-Latest-Final-Year-Projects-with-Source-Code

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.

  • Updated Apr 6, 2024
  • Jupyter Notebook

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.

  • Updated Mar 13, 2024
  • Jupyter Notebook
Network-Anomaly-Detection-System-Project-Machine-Learning-Project

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

  • Updated Jan 22, 2026

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.

  • Updated Mar 12, 2026
  • Python

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