Modular Machine Learning-Based Intrusion Detection System

This document is a technical report that presents the Hybrid Anomaly Detection Model (HADM), a machine learning-based architecture designed for identifying and filtering malicious network activities. The report details the components of HADM, which include a protocol analyzer and various classification and clustering algorithms. It explains how these components work together to enhance intrusion detection capabilities, particularly in the context of evolving cyber threats. The architecture is designed to be scalable and adaptable, addressing the challenges posed by modern network environments, including 5G and IoT. The report outlines the system architecture, including the processes for analyzing network traffic, classifying known attacks, and clustering unknown traffic. It also discusses the performance metrics used to evaluate the system's effectiveness, such as precision and recall, and describes the real-time visualization capabilities of the HADM dashboard. The report concludes by validating HADM as a comprehensive solution for contemporary network security challenges.