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The explosive growth and increasing sophistication of Android malware call for new defensive techniques that are capable of protecting mobile users against novel threats. To combat the evolving Android malware attacks, systems of HinDroid and AiDroid have demonstrated the success of heterogeneous graph (HG) based classifiers in Android malware detection; however, their success may also incentivize attackers to defeat HG based models to bypass the detection. By far, there has no work on adversarial attack and/or defense on HG data. In this paper, we explore the robustness of HG based model in Android malware detection at the first attempt. In particular, based on a generic HG based classifier, (1) we first present a novel yet practical adversarial attack model (named HG-Attack) on HG data by considering Android malware attackers' current capabilities and knowledge; (2) to effectively combat the adversarial attacks on HG, we then propose a resilient yet elegant defense paradigm (named Rad-HGC) to enhance robustness of HG based classifier in Android malware detection. Promising experimental results based on the large-scale and real sample collections from Tencent Security Lab demonstrate the effectiveness of our developed system αCyber, which integrates our proposed defense model Rad-HGC that is resilient against practical adversarial malware attacks on the HG data performed by HG-Attack.

αCyber : Enhancing Robustness of Android Malware Detection System against Adversarial Attacks on Heterogeneous Graph based Model