Machine Learning Approaches for Network Intrusion Detection: An Evaluation of their Efficacy in Bolstering Security

Abstract

Abstract: In today's digital environment, securing networked systems is critical due to the increasing sophistication and frequency of cyberattacks. Network Intrusion Detection Systems (NIDS) are essential for detecting and addressing unauthorized and harmful activities within a network. NIDS function by continuously monitoring network traffic and analyzing data packets for signs of suspicious behavior or known attack patterns. This paper provides an in-depth examination of NIDS, focusing on the application of three machine learning algorithms—K Neighbors Classifier, Logistic Regression, and Random Forest Classifier—to create a robust model for network intrusion detection using the NSL-KDD dataset. Our findings highlight the superior performance of the Random Forest Classifier, which achieved an accuracy of 99.31%, proving its effectiveness in differentiating between normal and malicious traffic. The analysis of ICMP, TCP, and UDP protocols reveals unique attack patterns, underscoring the need for protocol-specific security measures. Additionally, the study emphasizes the importance of integrating NIDS with other security systems for a multi-layered defense strategy and the crucial role of skilled personnel in managing and interpreting NIDS alerts. The results advocate for ongoing innovation and adaptation in NIDS technologies and strategies to effectively counter evolving cyber threats