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Android has become the most popular mobile operating system. Correspondingly,an increasing number of Android malware has been developed and spread to stealusers' private information. There exists one type of malware whose benignbehaviors are developed to camouflage malicious behaviors. The maliciouscomponent occupies a small part of the entire code of the application (app forshort), and the malicious part is strongly coupled with the benign part. Inthis case, the malware may cause false negatives when malware detectors extractfeatures from the entire apps to conduct classification because the maliciousfeatures of these apps may be hidden among benign features. Moreover, someprevious work aims to divide the entire app into several parts to discover themalicious part. However, the premise of these methods to commence app partitionis that the connections between the normal part and the malicious part areweak.  In this paper, we call this type of malware as Android covert malware andgenerate the first dataset of covert malware. To detect them, we first conductstatic analysis to extract the call graphs. Through the deep analysis ongraphs, we observe that although the correlations between the normal part andthe malicious part in these graphs are high, the degree of these correlationshas a distribution. Based on the observation, we design HomDroid to detectcovert malware by analyzing the homophily of call graphs. We identify the idealthreshold of correlation to distinguish the normal part and the malicious partbased on the evaluation results on a dataset of 4,840 benign apps and 3,385covert malicious apps. According to our evaluation results, HomDroid is capableof detecting 96.8% of covert malware while the False Negative Rates of anotherfour state-of-the-art systems (i.e., PerDroid, Drebin, MaMaDroid, and IntDroid)are 30.7%, 16.3%, 15.2%, and 10.4%, respectively.

HomDroid: Detecting Android Covert Malware by Social-Network Homophily Analysis