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  • Open Access

    ARTICLE

    High Performance Classification of Android Malware Using Ensemble Machine Learning

    Pagnchakneat C. Ouk1, Wooguil Pak2,*

    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 381-398, 2022, DOI:10.32604/cmc.2022.024540

    Abstract Although Android becomes a leading operating system in market, Android users suffer from security threats due to malwares. To protect users from the threats, the solutions to detect and identify the malware variant are essential. However, modern malware evades existing solutions by applying code obfuscation and native code. To resolve this problem, we introduce an ensemble-based malware classification algorithm using malware family grouping. The proposed family grouping algorithm finds the optimal combination of families belonging to the same group while the total number of families is fixed to the optimal total number. It also adopts unified feature extraction technique for… More >

  • Open Access

    ARTICLE

    Unified Detection of Obfuscated and Native Android Malware

    Pagnchakneat C. Ouk1, Wooguil Pak2,*

    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3099-3116, 2022, DOI:10.32604/cmc.2022.020202

    Abstract The Android operating system has become a leading smartphone platform for mobile and other smart devices, which in turn has led to a diversity of malware applications. The amount of research on Android malware detection has increased significantly in recent years and many detection systems have been proposed. Despite these efforts, however, most systems can be thwarted by sophisticated Android malware adopting obfuscation or native code to avoid discovery by anti-virus tools. In this paper, we propose a new static analysis technique to address the problems of obfuscating and native malware applications. The proposed system provides a unified technique for… More >

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