Abstract
Number of smartphone users of android based devices is growing rapidly. Because of the popularity of the android market malware attackers are focusing in this area for their bad intentions. Therefore, android malware detection has become a demanding and rising area to research in information security. Researchers now can effortlessly detect the android malware whose patterns have formerly been recognized. At present, malware attackers commenced to use obfuscation techniques to make the malwares incomprehensible to malware detectors. For this motive, it is urgent to identify the pattern that is used by attackers to obfuscate the malwares. A large-scale investigation has been performed in this paper by developing python scripts to extract the pattern of app components from an obfuscated android malware dataset. Ultimately, the patterns in a matrix form has been established and stored in a Comma Separated Values (CSV) file which will conduct to the primary basis of detecting the obfuscated malwares.
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Russel, M.O.F.K., Rahman, S.S.M.M., Islam, T. (2020). A Large-Scale Investigation to Identify the Pattern of App Component in Obfuscated Android Malwares. In: Bhattacharjee, A., Borgohain, S., Soni, B., Verma, G., Gao, XZ. (eds) Machine Learning, Image Processing, Network Security and Data Sciences. MIND 2020. Communications in Computer and Information Science, vol 1241. Springer, Singapore. https://doi.org/10.1007/978-981-15-6318-8_42
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