Adversarial Filter on Malware Domain

This research investigates the feasibility of employing adversarial filtering techniques to counter evasion attacks on a Support Vector Machine malware detection model. Exploring the feasibility of packed use and the potential differences in performance when incorporating the adversarial classifier before or after the primary model. The research aims to demonstrate the efficient operation of the models on a host machine while utilizing medium to lower computational resources.