Intelligent Malware Adversarial Technology Based on Deep Learning

The continuous evolution of artificial intelligence (AI) and deep learning (DL) has reshaped the landscape of cybersecurity. This study explores the mechanisms, structures, and adversarial behaviors of intelligent malware that leverage AI models to evade traditional security systems. A comprehensive deep learning-based framework is proposed for malware detection, attack prediction, and defense optimization. Through empirical experiments using a hybrid dataset of 20,000 samples, our framework achieves 99.1% accuracy and demonstrates high robustness against adversarial perturbations. The study also highlights the vulnerability of DL models to adversarial attacks and backdoor threats, emphasizing the dual-use nature of AI technology. The results underscore the necessity of integrating explainable AI and federated defense mechanisms for next-generation cyber resilience.