Breaking the Anti-malware: EvoAAttack Based on Genetic Algorithm Against Android Malware Detection Systems
Book Chapter
Rathore, H, B, P, Iyengar, SS et al. Breaking the Anti-malware: EvoAAttack Based on Genetic Algorithm Against Android Malware Detection Systems
. 14077 LNCS -550.
Rathore, H, B, P, Iyengar, SS et al. Breaking the Anti-malware: EvoAAttack Based on Genetic Algorithm Against Android Malware Detection Systems
. 14077 LNCS -550.
Today, android devices like smartphones, tablets, etc., have penetrated very deep into our modern society and have become an integral part of our daily lives. The widespread adoption of these devices has also garnered the immense attention of malware designers. Many recent reports suggest that existing malware detection systems cannot cope with current malware challenges and thus threaten the android ecosystem’s stability and security. Therefore, researchers are now turning towards android malware detection systems based on machine and deep learning algorithms, which have shown promising results. Despite their superior performance, these systems are not immune to adversarial attacks, highlighting a research gap in this field. Therefore, we design and develop EvoAAttack based on a genetic algorithm to expose vulnerabilities in state-of-the-art malware detection systems. The EvoAAttack is a targeted false-negative evasion attack strategy for the grey-box scenario. The EvoAAttack aims to convert malicious android applications (by adding perturbations) into adversarial applications that can deceive detection systems. The EvoAAttack agent is designed to convert maximum malware into adversarial applications with minimum perturbations while maintaining syntactic, semantic, and behavioral integrity. We tested EvoAAttack against thirteen distinct malware detection systems based on machine and deep learning algorithms from four different categories. The EvoAAttack was able to convert an average of of malware applications (with a maximum of five perturbations) into adversarial applications (malware variants). These adversarial applications force misclassifications and reduce the average accuracy of thirteen malware detection systems from to . Later we also designed a defense strategy (defPCA) to counter the adversarial attacks. The defPCA defense reduces the average forced misclassification rate from to against the same thirteen malware detection systems. Finally, we conclude that threat modeling improves both detection performance and adversarial robustness of malware detection systems.
