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Differences in Android Behavior Between Real Device and Emulator: A Malware Detection Perspective.

, , and . IoTSMS, page 399-404. IEEE, (2019)

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Security-level classification for confidential documents by using adaptive neuro-fuzzy inference systems., , and . Expert Syst. J. Knowl. Eng., 30 (3): 233-242 (2013)Leveraging the first line of defense: a study on the evolution and usage of android security permissions for enhanced android malware detection., , and . J. Comput. Virol. Hacking Tech., 19 (1): 65-96 (March 2023)On the application of active learning for efficient and effective IoT botnet detection., and . Future Gener. Comput. Syst., (April 2023)The cyber-insurance market in Norway., , and . Inf. Comput. Secur., 28 (1): 54-67 (2020)Security Level Classification of Confidential Documents Written in Turkish., and . UCMedia, volume 40 of Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, page 329-334. Springer, (2009)Hybrid Feature Selection Models for Machine Learning Based Botnet Detection in IoT Networks., , and . CW, page 324-327. IEEE, (2019)A comparative framework for cyber threat modelling: case of healthcare and industrial control systems., , , and . Int. J. Crit. Infrastructures, 19 (5): 405-431 (2023)An Ontology Engineering Case Study for Advanced Digital Forensic Analysis., , and . MEDI, volume 12732 of Lecture Notes in Computer Science, page 67-74. Springer, (2021)KronoDroid: Time-based Hybrid-featured Dataset for Effective Android Malware Detection and Characterization., , and . Comput. Secur., (2021)Threat modeling of industrial control systems: A systematic literature review., , and . Comput. Secur., (January 2024)