ENHANCED INTRUSION DETECTION SYSTEM IN COMMUNICATION SYSTEM USING WEIGHTED FEATURE SELECTION AND A VOTING CLASSIFIER

Abstract

The rapid growth of communication networks and internet-based applications has significantly increased the risk of cyber-attacks, making Intrusion Detection Systems (IDS) a critical component of network security. However, modern IDS face challenges in handling high-dimensional network traffic data, which often contains redundant and irrelevant features that degrade detection performance and increase computational cost.
This project proposes an enhanced intrusion detection system that integrates CHI-REV weighted feature selection with a Voting Classifier-based ensemble learning