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  • Open Access

    ARTICLE

    Detecting Android Botnet Applications Using Convolution Neural Network

    Mamona Arshad1, Ahmad Karim1, Salman Naseer2, Shafiq Ahmad3, Mejdal Alqahtani3, Akber Abid Gardezi4, Muhammad Shafiq5,*, Jin-Ghoo Choi5

    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 2123-2135, 2023, DOI:10.32604/cmc.2022.028680 - 29 November 2023

    Abstract The exponential growth in the development of smartphones and handheld devices is permeated due to everyday activities i.e., games applications, entertainment, online banking, social network sites, etc., and also allow the end users to perform a variety of activities. Because of activities, mobile devices attract cybercriminals to initiate an attack over a diverse range of malicious activities such as theft of unauthorized information, phishing, spamming, Distributed Denial of Services (DDoS), and malware dissemination. Botnet applications are a type of harmful attack that can be used to launch malicious activities and has become a significant threat… More >

  • Open Access

    ARTICLE

    A Learning Model to Detect Android C&C Applications Using Hybrid Analysis

    Attia Qammar1, Ahmad Karim1,*, Yasser Alharbi2, Mohammad Alsaffar2, Abdullah Alharbi2

    Computer Systems Science and Engineering, Vol.43, No.3, pp. 915-930, 2022, DOI:10.32604/csse.2022.023652 - 09 May 2022

    Abstract Smartphone devices particularly Android devices are in use by billions of people everywhere in the world. Similarly, this increasing rate attracts mobile botnet attacks which is a network of interconnected nodes operated through the command and control (C&C) method to expand malicious activities. At present, mobile botnet attacks launched the Distributed denial of services (DDoS) that causes to steal of sensitive data, remote access, and spam generation, etc. Consequently, various approaches are defined in the literature to detect mobile botnet attacks using static or dynamic analysis. In this paper, a novel hybrid model, the combination More >

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