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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Robust Android Malicious Community Fingerprinting,tinct authors and certificates, are most likely to be malicious if they are highly similar. Because the adversary usually repackages multiple app packages with the same malicious payload to hide it from anti-malware and vetting systems. Consequently, it is difficult to detect such malicious payloads

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Android Malware Fingerprinting Using Dynamic Analysis,ion at the end host, in . (2009), pp. 351–366) used specific features to build behavioral graphs for Win32 malware detection. The security features may change based on the execution environment despite the target platform. For instance, the authors in Chen et al. (Stormdroid: A streaminglized machin

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Fingerprinting Cyber-Infrastructures of Android Malware,sub-cyber-infrastructures. To this end, . leverages cyber-threat intelligence that is derived from various sources such as spam, Windows malware, darknet, and passive DNS to ascribe cyber-threats to the corresponding cyber-infrastructure. Accordingly, the input of . framework is made of malware samp

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Conclusion,id malware were discovered in 2014 and 2015, respectively. Nowadays, the number of malware samples reaches millions per month and is growing exponentially over time. In this context, it is a desideratum to elaborate scalable, robust, and accurate techniques and frameworks that tackle two specific pr

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Advances in Information Securityhttp://image.papertrans.cn/a/image/157072.jpg

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E. Frey,K. Kroy,J. Wilhelm,E. Sackmannng fingerprints such as MD5 and SHA1. Still, the fuzzy fingerprint change is virtually linear to the change in the binary content. In other words, smaller changes in the static content of the malware will cause a minor change in the computed fuzzy fingerprint. In the context of cybersecurity, this i

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https://doi.org/10.1007/978-3-540-76376-5ection until signature generation by anti-malware vendors. The larger the window is, the more time the malicious apps are given to spread over the users’ devices. Current state-of-the-art techniques have a large analysis window due to the significant number of Android malware appearing daily. Beside