DANdroid

Abstract

We present DANdroid, a novel Android malware detection model using a deep learning Discriminative Adversarial Network (DAN) that classifies both obfuscated and unobfuscated apps as either malicious or benign. Our method, which we empirically demonstrate is robust against a selection of four prevalent and real-world obfuscation techniques, makes three contributions. Firstly, an innovative application of discriminative adversarial learning results in malware feature representations with a strong degree of resilience to the four obfuscation techniques. Secondly, the use of three feature sets; raw opcodes, permissions and API calls, that are combined in a multi-view deep learning architecture to increase this obfuscation resilience. Thirdly, we demonstrate the potential of our model to generalize over rare and future obfuscation methods not seen in training. With an overall dataset of 68,880 obfuscated and unobfuscated malicious and benign samples, our multi-view DAN model achieves an average F-score of 0.973 that compares favourably with the state-of-the-art, despite being exposed to the selected obfuscation methods applied both individually and in combination. CCS CONCEPTS • Security and privacy → Malware and its mitigation; • Computing methodologies → Adversarial learning; Multi-task learning; Neural networks;

A Comprehensive Survey on Machine Learning Techniques for Android Malware Detection

“…Millar et al [37] presented DANdroid, a mobile malware detection model which uses deep learning to classify apps. DANdroid capitalizes on a triad of features, namely Opcodes, permissions, and API calls.…”