Enhancing Robustness of Deep Neural Networks Against Adversarial Malware Samples: Principles, Framework, and AICS'2019 Challenge

Abstract

Malware continues to be a major cyber threat, despite the tremendous effort that has been made to combat them. The number of malware in the wild steadily increases over time, meaning that we must resort to automated defense techniques. This naturally calls for machine learning based malware detection. However, machine learning is known to be vulnerable to adversarial evasion attacks that manipulate a small number of features to make classifiers wrongly recognize a malware sample as a benign one. The state-of-the-art is that there are no effective countermeasures against these attacks. Inspired by the AICS'2019 Challenge, we systematize a number of principles for enhancing the robustness of neural networks against adversarial malware evasion attacks. Some of these principles have been scattered in the literature, but others are proposed in this paper for the first time. Under the guidance of these principles, we propose a framework and an accompanying training algorithm, which are then applied to the AICS'2019 challenge. Our experimental results have been submitted to the challenge organizer for evaluation.

arXiv comment8 pages, 4 figures, AICS 2019; for the fully-fledged version, please see arxiv:2004.07919

References in corpus (8)

• Explaining and Harnessing Adversarial Examples
• A systematic study of the class imbalance problem in convolutional neural networks
• Smart Augmentation - Learning an Optimal Data Augmentation Strategy
• Generating Adversarial Malware Examples for Black-Box Attacks Based on GAN
• Adversarial Feature Selection against Evasion Attacks
• Why Do Adversarial Attacks Transfer? Explaining Transferability of Evasion and Poisoning Attacks
• Adversarial Deep Learning for Robust Detection of Binary Encoded Malware
• HashTran-DNN: A Framework for Enhancing Robustness of Deep Neural Networks against Adversarial Malware Samples

Cited by in corpus (3)

• MAB-Malware: A Reinforcement Learning Framework for Attacking Static Malware Classifiers
• Learnability and Robustness of Shallow Neural Networks Learned With a Performance-Driven BP and a Variant PSO For Edge Decision-Making
• Playing to Learn Better: Repeated Games for Adversarial Learning with Multiple Classifiers