Applications of Game Theory and Advanced Machine Learning Methods for Adaptive Cyberdefense Strategies in the Digital Music Industry

Abstract

As the likelihood and impact of cyber-attacks continue to grow, organizations realize the need to invest in specialized methods to protect their digital data and the information they circulate or manage. Due to its broad use, game theory has evolved into a concept that can be applied practically while analyzing and modifying existing cyber protection methods to arrive at the best possible conclusions. This study presents an innovative hybrid model that combines game theory and advanced machine learning methods for adaptive cyber defense strategies. Specifically, a repetitive game methodology is implemented to analyze cyber-attacks and model behaviors and study how defenders and attackers make decisions in a competing field. Based on Bayesian inference, the proposed method can predict the next steps in the game to produce the appropriate countermeasures and implement the best cyber defense strategies that govern an organization. The suggested system introduced to the academic literature for the first time was successfully tested in a particular application scenario involving the digital music industry and coping with impending cyber-attacks.