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dc.contributor.authorMohammed K. Alzaylaeeen
dc.contributor.authorYerima, Suleimanen
dc.contributor.authorSezer, Sakiren
dc.date.accessioned2018-10-29T10:31:45Z
dc.date.available2018-10-29T10:31:45Z
dc.date.issued2017-06
dc.identifier.citationAlzaylaee, M. K., Yerima, S. Y., and Sezer, S. (2017) Improving Dynamic Analysis of Android Apps Using Hybrid Test Input Generation. International Conference on Cyber Security and Protection of Digital Services (Cyber Security 2017)en
dc.identifier.isbn9781509050635
dc.identifier.urihttp://hdl.handle.net/2086/16863
dc.description.abstractThe Android OS has become the most popular mobile operating system leading to a significant increase in the spread of Android malware. Consequently, several static and dynamic analysis systems have been developed to detect Android malware. With dynamic analysis, efficient test input generation is needed in order to trigger the potential run-time malicious behaviours. Most existing dynamic analysis systems employ random-based input generation methods usually built using the Android Monkey tool. Random-based input generation has several shortcomings including limited code coverage, which motivates us to explore combining it with a state-based method in order to improve efficiency. Hence, in this paper, we present a novel hybrid test input generation approach designed to improve dynamic analysis on real devices. We implemented the hybrid system by integrating a random based tool (Monkey) with a state based tool (DroidBot) in order to improve code coverage and potentially uncover more malicious behaviours. The system is evaluated using 2,444 Android apps containing 1222 benign and 1222 malware samples from the Android malware genome project. Three scenarios, random only, state-based only, and our proposed hybrid approach were investigated to comparatively evaluate their performances. Our study shows that the hybrid approach significantly improved the amount of dynamic features extracted from both benign and malware samples over the state-based and commonly used random test input generation method.en
dc.language.isoenen
dc.subjectAndroiden
dc.subjectmalwareen
dc.subjectmalware detectionen
dc.subjectcode coverageen
dc.subjectapplication securityen
dc.subjecttest input generation methodsen
dc.subjectandroid monkey toolen
dc.subjectapi callsen
dc.titleImproving Dynamic Analysis of Android Apps Using Hybrid Test Input Generationen
dc.typeConferenceen
dc.identifier.doihttps://dx.doi.org/10.1109/CyberSecPODS.2017.8074845
dc.peerreviewedYesen
dc.funderN/Aen
dc.projectidN/Aen
dc.cclicenceN/Aen
dc.researchinstituteCyber Technology Institute (CTI)en


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