Evolutionary Deep-Learning Malware Classifiers
Sabre Didi, Geoff Nitschke · 2025
Malware attacks remain a critical cyber-security concern, necessitating robust solutions for both individual users and organizations. Deep learning methods have become pervasive tools for malware detection and classification. However, the evolution of malware into sophisticated forms that aim to elude detection poses a formidable challenge to traditional deep-learning methods. Existing techniques for generating adversarial samples often rely on manual feature extraction and white-box models, introducing a gap between the generated samples and real-world scenarios. In response to these challenges, we propose an innovative approach leveraging evolutionary learning for the generation of adversarial samples. Our approach uses a three-step process for malware detection. First, a trained deep-learning malware classifier categorizes samples as benign or malicious. Second, an evolutionary adversarial learning approach trains and generates new malware samples. Third, competitive coevolution facilitates automated adaptation of malware detection agents that are robust against attacks. We evaluate the efficacy of our approach for adaptive malware detection via benchmark evaluations with an established deep-learning classifier.