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Building behavior profiles of Android applications (apps) with holistic, richand multi-view information (e.g., incorporating several semantic views of anapp such as API sequences, system calls, etc.) would help catering downstreamanalytics tasks such as app categorization, recommendation and malware analysissignificantly better. Towards this goal, we design a semi-supervisedRepresentation Learning (RL) framework named apk2vec to automatically generatea compact representation (aka profile/embedding) for a given app. Morespecifically, apk2vec has the three following unique characteristics which makeit an excellent choice for largescale app profiling: (1) it encompassesinformation from multiple semantic views such as API sequences, permissions,etc., (2) being a semi-supervised embedding technique, it can make use oflabels associated with apps (e.g., malware family or app category labels) tobuild high quality app profiles, and (3) it combines RL and feature hashingwhich allows it to efficiently build profiles of apps that stream over time(i.e., online learning). The resulting semi-supervised multi-view hashembeddings of apps could then be used for a wide variety of downstream taskssuch as the ones mentioned above. Our extensive evaluations with more than42,000 apps demonstrate that apk2vec's app profiles could significantlyoutperform state-of-the-art techniques in four app analytics tasks namely,malware detection, familial clustering, app clone detection and apprecommendation.

apk2vec: Semi-supervised multi-view representation learning for profiling Android applications