Game-Theoretic Malware Detection : Adversarial Neural Networks for Enhanced Real-Time Security

Abstract

The current cybersecurity threats rapidly advance as digital adversaries use complex malware to bypass traditional security detection methods. A new malware detection system based on game theory and neural adversarial AGMA presents techniques to enhance immediate security measures. Our model describes the attacker-defender relationship through a non-cooperative game structure that shows how malware modifies its techniques to escape detection as the security classifier enhances defense strategies. Generative adversarial networks (GANs) let us create simulations of improved evasion techniques that train an advanced malware detection system to detect previously unknown attacks. Review results show the proposed system achieves superior adversarial malware detection performance using benchmark malware datasets over traditional machine learning models. Research findings show that game-theoretic adversarial learning methods enhance real-time cybersecurity systems' ability to resist sophisticated evolving threats effectively.