Adaptive malware detection under concept drift: an evolutionary deep learning framework with GA-based retraining
The continuous evolution of malware can lead to performance degradation in static detection models; a phenomenon commonly associated with concept drift. This study presents an adaptive retraining approach that addresses concept drift while improving the performance of deep learning-based malware classifiers. The propos...