Advanced Modeling and Predictive Analysis of Cyber Hacking Breaches: Techniques and Insights

Abstract

The examination of cybersecurity incident datasets serves as a fundamental approach for enhancing our comprehension of threat landscape dynamics. This emerging research domain presents numerous opportunities for investigation. Our study presents a comprehensive statistical examination of breach incident data spanning twelve years (2005-2017), focusing specifically on cyber hacking events and malware-based attacks. Our findings challenge existing literature by demonstrating that both breach occurrence intervals and incident magnitudes require modeling through stochastic processes rather than traditional distribution methods, due to their inherent autocorrelation characteristics. We introduce specialized stochastic process frameworks designed to model both temporal patterns and breach magnitudes effectively. Our models demonstrate predictive capabilities for both timing and scale of future incidents. To gain comprehensive understanding of hacking breach evolution, we perform extensive qualitative and quantitative trend evaluations. Our research yields significant cybersecurity findings, particularly revealing that cyber threat frequency is escalating, while the severity of individual incidents remains relatively stable.