Verifying Anti-Security Policies Learnt from Android Malware Families

Wei Chen, Charles Sutton, David Aspinall, Andrew Gordon, Qi Shen, Igor Muttik

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Android malware has been increasingly identified and organised into families [28, 36], e.g., Geinimi, Basebridge, Spitmo, Zitmo, and Ginmaster, etc. This human-decided organisation was based on some unexpected behaviours exhibited in malware instances, e.g., intercept incoming messages then send them out via Internet connections, load classes from a hidden payload then execute commands from remote servers, and send premium messages constantly, and so on;and malware instances in one family share some common unexpected behaviours [17, 29]. We study the problem of verifying Android applications to deny these behaviours. That is, (a) to formalise and learn unexpected behaviours from malware instances exploiting their family information, so-called anti-security policies; (b) and verify target applications against these policies efficiently, so as to decide whether a target application has any unexpected behaviour. Our main contributions are : (a) we implement a static analysis tool to construct an extended B¨ uchi automaton for each Android application to approximate its behaviours, considering a broad range of features of Java and the Android framework [3];(b) we develop an efficient machine-learning-centred method to construct sub-automata as anti-security policies from thousands of malware instances across hundreds of malware families; (c) we demonstrate the effectiveness of antisecurity policy verification by showing how it helps reveal covert channels in a scenario of collusion attacks. We show that using the verification results against anti-security policies as input features, the classification performance on new malware detections is improved dramatically, in particular, the precision and recall are respectively 8% and 51% better than those using APIs calls and permissions as input features. We compare anti-security policies for malware families to their manual descriptions, which have been produced by malware analysts or third-party researchers [1, 2, 4, 5, 23],and demonstrate they compare well to these descriptions. This research has several potential benefits, including: help people get better understanding of potential threats hidden in mobile applications; provide hints for malware analysts before more expensive investigation; support automatic generation of malware analysis reports; and provide clear and friendly references for security policy designers, etc.
Original languageEnglish
Title of host publicationFourth International Seminar on Program Verification, Automated Debugging and Symbolic Computation
Number of pages6
Publication statusPublished - 21 Oct 2015


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