INTRUSION DETECTION WITH MACHINE LEARNING COMPARISON ANALYSIS

Abstract: Machine learning techniques have brought about a revolution in various fields, with a significant impact on cyber security. In the face of growing cyber threats, the need for effective intrusion detection systems (IDS) has become more crucial than ever. These systems play a vital role in the timely and automatic detection and classification of cyber attacks, at both the network-level and the host-level. However, traditional IDS, which rely on conventional machine learning methods, often fall short in terms of reliability and accuracy.As the number of network-related applications, programs, and services continues to grow, so do the associated network security issues. Safeguarding the network against malicious activities is a challenging and critical task. In order to maintain a secure network environment, an effective system for detecting and identifying any suspicious activity is essential. This system is commonly known as an Intrusion Detection System (IDS).