Enhancing ML Model Resilience to Time- Evolving Data in Malware Detection Systems with Adversarial Learning

Malware's rapid evolution and increasing sophistication challenge traditional static and dynamic detection methods, which struggle with the high volume of new threats. Machine learning (ML) techniques offer improved detection by automatically identifying malicious patterns, but their efficacy degrades over time as malware evolves. This study addresses performance deterioration in ML-based malware detection models with time-evolving data. We propose combining feature engineering and adversarial training to enhance model resilience. By generating training data mutations, our method helps models better recognize true malware indicators, maintaining high performance over time.