Performance evaluation of machine learning models for intrusion detection system

Abstract

In order to improve cybersecurity, this final year project investigates the use of machine learning (ML) in intrusion detection systems (IDS). Its main goal is to create an intelligent intrusion detection system (IDS) that can accurately identify and categorize network intrusions. The study demonstrates the efficacy of these methods in enhancing threat detection and security resilience through the application and assessment of multiple machine learning models.