The role of predictive analytics in cybersecurity: Detecting and preventing threats

Abstract

This paper examines the application of predictive analytics in the field of cybersecurity, focusing on its role in improving threat detection, prevention, and overall security measures. The scope of this review encompasses an overview of current cybersecurity challenges, the fundamental components of predictive analytics, and its practical applications. Methodologically, the review synthesizes findings from various case studies and research articles, highlighting the integration of machine learning algorithms, data preprocessing techniques, and performance metrics in predictive models. Key findings indicate that predictive analytics significantly enhances cybersecurity by enabling early detection of potential threats and facilitating proactive security measures. Techniques such as neural networks, decision trees, and support vector machines are employed to analyze historical data and identify patterns indicative of future threats. Despite its advantages, challenges such as data privacy, technical limitations, and issues related to false positives and negatives are noted. The review concludes that predictive analytics is a crucial tool in the ongoing battle against cyber threats, offering valuable insights for future research and practice. Advancements in predictive models and their integration with emerging technologies hold the potential to further strengthen cybersecurity defenses. The significance of predictive analytics lies in its ability to provide actionable intelligence, thereby improving the efficacy of cybersecurity measures and enhancing organizational resilience against evolving threats.