Research on AI-Based Techniques for Detecting Complex Malicious Code Behavior

Detecting complex malware poses a significant challenge to cybersecurity, as traditional methods face limitations when dealing with diverse and evolving attacks. This paper proposes an AI-based framework for detecting complex malicious code behavior, combining static and dynamic analysis and utilizing technologies such as deep learning, natural language processing, and reinforcement learning. This approach significantly enhances detection accuracy and robustness. Experimental results indicate that the system performs exceptionally well against zero-day attacks and new malware variants, achieving a detection accuracy of 97.8% and demonstrating rapid adaptation to new threats. The study shows that the proposed framework offers significant advantages in addressing complex malware behavior and provides important insights for the future development of cybersecurity technologies.