Machine Learning for Cybersecurity: Detecting and Preventing Threats

Abstract

In the evolving landscape of cybersecurity, the application of machine learning (ML) has emerged as a pivotal tool in detecting and preventing a myriad of cyber threats. This paper explores the integration of ML algorithms in cybersecurity frameworks to enhance the detection, analysis, and mitigation of threats such as malware, phishing, and network intrusions. By leveraging vast datasets, machine learning models can identify anomalous patterns and behaviors indicative of potential security breaches, thus enabling real-time threat detection and response. The study evaluates various ML techniques, including supervised learning, unsupervised learning, and reinforcement learning, highlighting their efficacy in different cybersecurity contexts. Furthermore, it addresses the challenges associated with the deployment of ML in cybersecurity, such as data quality, model interpretability, and adversarial attacks. Through a comprehensive review of recent advancements and case studies, the paper demonstrates how ML-driven approaches can significantly bolster cybersecurity defenses, providing a proactive and adaptive shield against increasingly sophisticated cyber threats.