An Efficient Malware Detection Approach for Malicious Android Application

  • MADIHA AMJAD HUSSAIN ´╗┐Department of Computer Science and Information Technology, NED University of Engineering and Technology, Karachi, Pakistan
  • Dr. Shariq Mahmood Khan Departmetn of Computer Science and Information Technology NEDUET, Karachi
  • SHAHZAD MEMON A.H.S. Bukhari Institute of Information & Communication Technology, Faculty of Engineering & Technology, University of Sindh, Jamshoro
  • SYED RAZA HUSSAIN Department of Electronics Engineering, University of Sindh, Jamshoro, Pakistan
Keywords: Malwares, Android, neural network, APK, security


Artificial intelligence is changing the game for cybersecurity, analyzing enormous amount of risky data, increasing response times and enlarging the abilities of under-resourced security tasks. While security as IT percentage grows at a fast pace, the cost of security beaches grows at a more rapid pace. The malware targeting Android is growing. Android systems holds more than 70 percent of the market share.This paper presents a simple APK analysis approach with the help of neural networks to identify malicious and benign application. The selected methodology is efficient in detecting malwares with an accuracy of 98.42% and false positive rate of 0.0121

How to Cite
MADIHA AMJAD HUSSAIN, Dr. Shariq Mahmood Khan, SHAHZAD MEMON, & SYED RAZA HUSSAIN. (2022). An Efficient Malware Detection Approach for Malicious Android Application. University of Sindh Journal of Information and Communication Technology , 5(3), 120-124. Retrieved from

Most read articles by the same author(s)

Obs.: This plugin requires at least one statistics/report plugin to be enabled. If your statistics plugins provide more than one metric then please also select a main metric on the admin's site settings page and/or on the journal manager's settings pages.