THE IMPACT OF BIG DATA ANALYTICS ON PREDICTIVE MODELING IN CYBERSECURITY THREAT DETECTION

Abstract

The rapid growth of digital systems has brought with it an unprecedented rise in cyber threats. Traditional methods of cybersecurity often struggle to keep pace with the complexity and volume of attacks. The emergence of Big Data Analytics (BDA) has introduced a paradigm shift in the way cybersecurity systems are designed, particularly in the realm of predictive modeling. By leveraging massive volumes of data generated by systems, networks, and devices, BDA allows for more accurate, real-time threat detection. This article explores how BDA enhances predictive modeling techniques used in cybersecurity, focusing on its ability to process and analyze large datasets to identify potential threats before they materialize. Through detailed exploration of machine learning (ML) models, anomaly detection, and advanced data processing techniques, we examine the role of Big Data in improving the effectiveness of cybersecurity systems. The paper also presents case studies and outlines future trends in integrating Big Data with cybersecurity tools, offering valuable insights into this critical field.