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|Title:||Apk2vec : semi-supervised multi-view representation learning for profiling Android applications||Authors:||Narayanan, Annamalai
|Keywords:||Engineering::Computer science and engineering||Issue Date:||2018||Source:||Narayanan, A., Soh, C., Chen, L., Liu, Y., & Wang, L. (2018). Apk2vec : semi-supervised multi-view representation learning for profiling Android applications. Proceedings of 2018 IEEE International Conference on Data Mining (ICDM), 357-366. doi:10.1109/ICDM.2018.00051||Abstract:||Building behavior profiles of Android applications (apps) with holistic, rich and multi-view information (e.g., incorporating several semantic views of an app such as API sequences, system calls, etc.) would help catering downstream analytics tasks such as app categorization, recommendation and malware analysis significantly better. Towards this goal, we design a semisupervised Representation Learning (RL) framework named apk2vec to automatically generate a compact representation (aka profile/embedding) for a given app. More specifically, apk2vec has the three following unique characteristics which make it an excellent choice for large-scale app profiling: (1) it encompasses information from multiple semantic views such as API sequences, permissions, etc., (2) being a semi-supervised embedding technique, it can make use of labels associated with apps (e.g., malware family or app category labels) to build high quality app profiles, and (3) it combines RL and feature hashing which allows it to efficiently build profiles of apps that stream over time (i.e., online learning). The resulting semi-supervised multi-view hash embeddings of apps could then be used for a wide variety of downstream tasks such as the ones mentioned above. Our extensive evaluations with more than 42,000 apps demonstrate that apk2vec's app profiles could significantly outperform state-of-the-art techniques in four app analytics tasks namely, malware detection, familial clustering, app clone detection and app recommendation.||URI:||https://hdl.handle.net/10356/142658||ISBN:||978-1-5386-9160-1||DOI:||10.1109/ICDM.2018.00051||Rights:||© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/ICDM.2018.00051.||Fulltext Permission:||open||Fulltext Availability:||With Fulltext|
|Appears in Collections:||EEE Conference Papers|
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