Deep Convolutional Generative Adversarial Networks for Intent-based Dynamic Behavior Capture - Jan, Salman; Musa, Shahrulniza; Ali, Toqeer; Alzahrani, Ali (International Journal of Engineering and Technology, 2018)
Malware analysis for Android systems has been the focus of considerable research in the past few years due to the large customer base moving towards Android, which has attracted a corresponding number of malware writers. Several techniques have been used to detect the malicious behavior of Android applications as well as that of the complete system. Machine-learning techniques have been used in the past to assess the behavior of an application using either static or dynamic analysis. However, for large scale Android malware analysis traditional machine learning techniques are not feasible. In this regard, many deep neural architectures have used static analysis. It has been shown that static analysis techniques can leave many malicious behaviors of an application unnoticed. In this paper, we used a new deep-learning architecture known as deep convolutional generative adversarial networks to measure the dynamic behavior of Android applications. More- over, we used the notion of Android intents as the parameter to measure the dynamic behavior of an application. We gathered a large set of intent-based behavior from more than 4,000 infected applications as well as 10 thousand applicationsâ€™ good behaviors on our modiï¬ed Oreo version of Android. We received an F1 score of 0.996 and AUC curve of 0.993, which is almost the same as those received by many state- of-the-art works using machine learning.