International Research Journal of Innovations in Engineering and Technology - IRJIET International Open Access, Monthly, Peer Reviewed, Reputed Journal ISSN (online): 2581-3048

Impact Factor (2025): 6.9

DOI Prefix: 10.47001/IRJIET

Opcode-Based Android Malware Detection Using Machine Learning Techniques

Abstract

Today Android applications are widely used by billions of users to perform different activity. So malware target the Android phone frequently. The new malware sample is a major issue and signature based technique are unable to find out new malware sample. In this paper author will appliance an approach to detect the unfamiliar Android malware using machine learning techniques with a high detection rate. We adopt sampling technique based on the sensitive opcodes sequence. Finally, we evaluate our method on AndroZoo dataset (15000 malware and 15000 benign Apks), and select the top 20 malware families for experiments. The experimental results show that the Total Accuracy 95.3%, 92.16%, and 92.6% with random forest, XGBosst, and Decision tree. 

Country : India

1 Sonal Pandey2 Ram Lal

  1. NITTTR Chandigarh, India
  2. Computer Service Centre, IIT Delhi, India

IRJIET, Volume 5, Issue 7, July 2021 pp. 56-61

doi.org/10.47001/IRJIET/2021.507010

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