MTMG: A Framework for Generating Adversarial Examples Targeting Multiple Learning-Based Malware Detection Systems

Lichen Jia, Yang Yang, Jiansong Li, Hao Ding, Jiajun Li, Ting Yuan, Lei Liu, Zihan Jiang

Abstract: As machine learning technology continues to advance rapidly, an increasing number of researchers are utilizing it in the field of malware detection. Despite the fact that learning-based malware detection systems (LB-MDS) outperform traditional feature-based detection methods in terms of both performance and detection speed, recent research has shown that they are susceptible to attacks from adversarial examples. However, the adversarial examples generated thus far have only been effective against individual LB-MDS and have not been able to simultaneously attack multiple LB-MDS.