Reinforcing Network Security Using Machine Learning Algorithms: A Comprehensive Study of Intrusion Detection Systems and Anomaly Detection Methods

Abstract

The advent of sophisticated cyber threats necessitates the implementation of robust network security mechanisms. Traditional intrusion detection systems (IDS) and anomaly detection methods often struggle to adapt to evolving attack patterns, leading to vulnerabilities in critical infrastructures. Machine learning (ML) algorithms offer a promising avenue for enhancing the efficacy of network security systems by enabling dynamic and adaptive threat identification. This study investigates the role of ML in reinforcing network security, focusing on its application in intrusion detection systems and anomaly detection frameworks. We explore various supervised, unsupervised, and reinforcement learning techniques, emphasizing their strengths and limitations. The discussion encompasses feature selection methods, model training approaches, and evaluation metrics used in IDS development. Furthermore, the paper highlights key challenges such as data imbalance, adversarial attacks, and the computational complexity of real-time implementations. By synthesizing insights from recent advancements, this research aims to provide a comprehensive understanding of how ML can be leveraged to build resilient network security systems. Concluding remarks suggest future research directions and potential strategies to address existing gaps.