KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS VOL. 14, NO. 6, Jun. 2020 2591 Copyright ⓒ 2020 KSII A Smart Framework for Mobile Botnet Detection Using Static Analysis Shahid Anwar1, Mohamad Fadli Zolkipli2, Vitaliy Mezhuyev3,*, Zakira Inayat4 1 Department of Software Engineering, The University of Lahore | 1-Km, Defence Road Bhobatian Chowk, Lahore, Pakistan. [e-mail:
[email protected]] 2 Faculty of Computing, College of Computing and Applied Sciences, Universiti Malaysia Pahang, Lebuhraya Tun Razak Gambang, 26300 Kuantan, Malaysia [e-mail:
[email protected]] 3 FH JOANNEUM University of Applied Sciences, Institute of Industrial Management Werk-VI-Straße 46, 8605 Kapfenberg, Austria [e-mail:
[email protected]] 4 Department of Computer Science, University of Engineering and Technology, Peshawar 2500, Pakistan [e-mail:
[email protected]] *Corresponding author: Vitaliy Mezhuyev Received May 12, 2019; revised November 11, 2019; accepted March 18, 2020; published June 30, 2020 Abstract Botnets have become one of the most significant threats to Internet-connected smartphones. A botnet is a combination of infected devices communicating through a command server under the control of botmaster for malicious purposes. Nowadays, the number and variety of botnets attacks have increased drastically, especially on the Android platform. Severe network disruptions through massive coordinated attacks result in large financial and ethical losses. The increase in the number of botnet attacks brings the challenges for detection of harmful software. This study proposes a smart framework for mobile botnet detection using static analysis. This technique combines permissions, activities, broadcast receivers, background services, API and uses the machine-learning algorithm to detect mobile botnets applications.