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A Multimodal Deep Learning Method for Android Malware Detection Using Various Features.

, , , , and . IEEE Trans. Inf. Forensics Secur., 14 (3): 773-788 (2019)

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N-gram Opcode Analysis for Android Malware Detection., , , and . Int. J. Cyber Situational Aware., 1 (1): 231-255 (2016)A Multimodal Deep Learning Method for Android Malware Detection Using Various Features., , , , and . IEEE Trans. Inf. Forensics Secur., 14 (3): 773-788 (2019)Android malware classification method: Dalvik bytecode frequency analysis., , , and . RACS, page 349-350. ACM, (2013)A hybrid MAC protocol for optimal channel allocation in large-scale wireless powered communication networks., , , and . EURASIP J. Wireless Comm. and Networking, (2018)Deep Android Malware Detection., , , , , , , , , and 1 other author(s). CODASPY, page 301-308. ACM, (2017)Broadening The Solution Space of Feature Selection in Time-interval-based Malware Detection., and . ANT/EDI40, volume 220 of Procedia Computer Science, page 769-775. Elsevier, (2023)Binary executable file similarity calculation using function matching., , , and . J. Supercomput., 75 (2): 607-622 (2019)Event-Driven Implicit Authentication for Mobile Access Control., , , and . NGMAST, page 248-255. IEEE, (2015)Weighted Harvest-Then-Transmit: UAV-Enabled Wireless Powered Communication Networks., , , , and . IEEE Access, (2018)Rule Indexing for Efficient Intrusion Detection Systems., , , and . WISA, volume 7115 of Lecture Notes in Computer Science, page 136-141. Springer, (2011)