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Android malware classification method: Dalvik bytecode frequency analysis.

, , , and . RACS, page 349-350. ACM, (2013)

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Similarity calculation method for user-define functions to detect malware variants., , , , , and . RACS, page 236-241. ACM, (2014)Rule Indexing for Efficient Intrusion Detection Systems., , , and . WISA, volume 7115 of Lecture Notes in Computer Science, page 136-141. Springer, (2011)Event-Driven Implicit Authentication for Mobile Access Control., , , and . NGMAST, page 248-255. IEEE, (2015)Android malware classification method: Dalvik bytecode frequency analysis., , , and . RACS, page 349-350. ACM, (2013)A Multimodal Deep Learning Method for Android Malware Detection Using Various Features., , , , and . IEEE Trans. Inf. Forensics Secur., 14 (3): 773-788 (2019)N-gram Opcode Analysis for Android Malware Detection., , , and . Int. J. Cyber Situational Aware., 1 (1): 231-255 (2016)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)Towards A Stateful Analysis Framework for Smart Grid Network Intrusion Detection., , and . ICS-CSR, BCS, (2016)Fast malware family detection method using control flow graphs., , , , and . RACS, page 287-292. ACM, (2011)