Titlebook: Android Malware Detection using Machine Learning; Data-Driven Fingerpr ElMouatez Billah Karbab,Mourad Debbabi,Djedjiga Mo Book 2021 The Edi

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https://doi.org/10.1007/978-3-540-76376-5ses. The state-of-the-art solutions, such as Chen et al., (Stormdroid: A streaminglized machine learning-based system for detecting android malware, in . (2016), pp. 377–388), Kharraz et al. (UNVEIL: A large-scale, automated approach to detecting ransomware, in . (2016), pp. 757–772) and Sgandurra e

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https://doi.org/10.1007/978-3-540-76376-5focuses on portable malware detection based on applying supervised machine learning on static analysis features in contrast to ., presented in Chap. ., in which we propose an unsupervised system based on static analysis features. While . provides a framework for malware clustering, aimed at market l

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https://doi.org/10.1007/978-3-540-76376-5chniques by introducing code randomization during training; (2) adaptation to operating system and malware change overtime by introducing the use of confidence-based decisions to collect adaptation datasets overtime. In this context, we identify several limitations and gaps in the state-of-the-art A

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https://doi.org/10.1007/978-3-540-76376-5running on smart devices are nowadays ubiquitous due to their convenience. For instance, users can presently use apps as Google Pay service to purchase products online and to make payments in retail stores. However, the growth of the mobile market apps has increased the concerns about the security o

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https://doi.org/10.1007/978-3-030-74664-3Android; malware detection; Fingerprinting; mobile security; cybersecurity; machine Learning

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978-3-030-74666-7The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl

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Resilient and Adaptive Android Malware Fingerprinting and Detection,chniques by introducing code randomization during training; (2) adaptation to operating system and malware change overtime by introducing the use of confidence-based decisions to collect adaptation datasets overtime. In this context, we identify several limitations and gaps in the state-of-the-art Android malware detection solutions.

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