Game Theory and Adversarial Machine Learning: Analyzing Strategic Interactions in Cybersecurity

Abstract

When it comes to cybersecurity, the way that attackers and defenders work together strategically is becoming more and more like how games work in general. Adversarial machine learning (AML) has become an important area of hacking. In AML, attackers use complex methods to avoid being caught and take advantage of flaws in machine learning models. The goal of this study is to give a full picture of how strategies combine in cybersecurity by looking at where game theory and AML meet. You can think of the strategic exchanges in cybersecurity as a game between attackers and defenders. Defenders want to keep systems and data safe, and enemies want to break into them for bad reasons. In this game, players have to make a lot of decisions. Defenders have to think ahead and prepare for possible attacks, while attackers change their plans all the time to avoid being caught and take advantage of defenses' weaknesses. Game theory gives us a way to formally model these interactions, which lets us look at the best tactics and results that are in balance. During AML, defenders use machine learning models to find and stop security risks, while attackers change these models using methods like escape attacks, poisoning attacks, and model inversion attacks. In these hostile tactics, there is a strategic element added to cybersecurity, where defenders must think about the goals and skills of attackers when creating and using defenses. This paper looks at the research that has already been done on using game theory to study hacking and how these models can be used to study how strategies combine in AML. It talks about different types of games, like rigid and dynamic games, and what they mean for protecting against threats from other players. Besides that, it looks into how uneven knowledge, doubt, and strategy learning affect the results of computer games. This paper helps us understand the strategy problems that both attackers and defenders face in cybersecurity better by combining game theory with AML. It shows how important it is to think strategically and build weapons that can change to deal with risks that change, and it sets the stage for future study in this field, which is changing very quickly.