Mal-LSGAN: An Effective Adversarial Malware Example Generation Model

Abstract

Various Machine Learning (ML) models have been developed for malware detection. But their widespread application is challenged by adversarial attacks using adversarial malware examples. Generative Adversarial Networks (GAN) is one of the effective approaches to help build possible unknown attacks and expose the vulnerability of targeted systems. The existing GAN-based ML models have the weaknesses of unstable training and low-quality adversarial examples. In this paper, we propose a novel Mal-LSGAN model to tackle these weaknesses. By using a Least Square (LS) loss function and new activation function combinations, Mal-LSGAN achieves a higher Attack Success Rate (ASR) and a lower True Positive Rate (TPR) in 6 ML detectors, compared with the existing MalGAN and Imp-MalGAN. In Multi-Layer Perceptron (MLP), Mal-LSGAN can even decrease TPR from 97.81% of original examples to 2.92% of adversarial examples. The experimental results also demonstrate that Mal-Lsgangets the preferable transferability of adversarial malware examples.