NAVIGATING CYBERSECURITY: A COMPREHENSIVE ANALYSIS OF MACHINE LEARNING IN CYBER ATTACK DETECTION

In the ever-evolving landscape of cyber threats, the integration of machine learning (ML) techniques has
emerged as a powerful tool for detecting and mitigating attacks across various sectors, such as the Internet of
Things (IoT) and Wireless Sensor Networks (WSN). This analysis paper examines several ML algorithms,
such as Random Forest (RF), Ridge Classifier, and Gaussian Naive Bayes, and their efficacy in enhancing
cyber-attack detection accuracy and efficiency. Emphasising the significance of preprocessing data and
feature extraction, our paper highlights the exceptional performance of hybrid models and the transformative
role of Multi-Agent Reinforcement Learning (MARL) in addressing the challenges posed by class imbalance
and rapidly evolving threats. This paper underscores the critical need for continuous innovation in
cybersecurity measures by showcasing the promising results achieved through these techniques. Ultimately,
our findings reveal that while various ML approaches have successfully detected cyber-attacks, the
implementation of MARL represents a significant advancement in developing robust and adaptive intrusion
detection systems.