Non-cooperative game theory with generative adversarial network for effective decision-making in military cyber warfare

Cyber warfare has become a critical domain of modern military operations, characterized by the constant interplay of offense and defense in the digital realm. This research explores a novel approach to enhance decision-making in military cyber warfare by integrating non-cooperative game theory and generative adversarial networks (GANs). A framework for simulating the strategic interactions between military units or cyber attackers is provided by non-cooperative game theory, where each entity makes independent decisions to maximize its interests. In this context, the study formulates cyber warfare scenarios as non-cooperative games, allowing for the analysis of optimal strategies and outcomes. The introduction of generative adversarial networks (GANs) introduces a machine learning dimension to the research. GANs have demonstrated their prowess in generating realistic data and adversarial training. GANs can simulate and predict cyberattack strategies and defense mechanisms in military cyber warfare. The primary objective of this research is to develop a decision-making framework that leverages non-cooperative game theory and GANs to enhance the effectiveness of military cyber operations. This framework encompasses strategic planning, cyberattack detection, and response strategies. It provides decision-makers valuable insights into cyber warfare’s dynamic and adversarial nature, facilitating more informed and robust decisions. The findings of this research have the potential to revolutionize military cyber warfare strategies, enabling proactive defense mechanisms and better preparedness against sophisticated cyber threats. Moreover, integrating non-cooperative game theory with GANs represents a promising avenue for advancing decision support systems in complex, dynamic environments.