Malware Detection and Defense Mechanism based on Deep Learning Algorithm

With software technology evolving, malware's diversity and polymorphism make detection more complicated, and traditional defenses lack dynamic response. This paper combines cross-modal Transformer, GAN, and reinforcement learning, fuses static and dynamic data, uses self-attention to capture malware polymorphism, GAN-generated samples to boost model robustness, and reinforcement learning for a dynamic response. Experiments show this approach outperforms traditional methods. The detection accuracy is 98.6%, the false alarm rate is 3.2%, and the missed alarm rate is 2.5%. It also improves robustness and defense success rate, offering an effective complex network malware protection solution.