DETECTION AND PREVENTION OF CYBER ATTACKS USING MACHINE LEARNING ALGORITHMS: A COMPREHENSIVE STUDY

Abstract

The rapid evolution of cyber threats has underscored the need for innovative methods to detect and prevent attacks. This study explores the application of machine learning algorithms in mitigating cybersecurity risks, focusing on their ability to identify patterns, detect anomalies, and predict potential threats in real-time. Various supervised, unsupervised, and reinforcement learning techniques are evaluated for their effectiveness in combating different types of cyber attacks, including phishing, malware, denial-of-service, and insider threats. The research also examines challenges such as data imbalance, feature selection, and computational complexity while proposing solutions to optimize algorithm performance. By leveraging case studies and experimental results, this study demonstrates the transformative potential of machine learning in enhancing cybersecurity frameworks and outlines future directions for research and implementation.