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DOI Prefix: 10.47001/IRJIET

Opcode-Based Android Malware Detection Using Machine Learning Techniques

Sonal PandeyNITTTR Chandigarh, IndiaRam LalComputer Service Centre, IIT Delhi, India

Vol 5 No 7 (2021): Volume 5, Issue 7, July 2021 | Pages: 56-61

International Research Journal of Innovations in Engineering and Technology

OPEN ACCESS | Research Article | Published Date: 31-07-2021

doi Logo doi.org/10.47001/IRJIET/2021.507010

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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. 
Keywords

Malware, Metamorphic malware, Android, Machine Learning


Citation of this Article

Sonal Pandey, Ram Lal, “Opcode-Based Android Malware Detection Using Machine Learning Techniques” Published in International Research Journal of Innovations in Engineering and Technology - IRJIET, Volume 5, Issue 7, pp 56-61, July 2021. Article DOI https://doi.org/10.47001/IRJIET/2021.507010

Licence Copyright (c) 2026 International Research Journal of Innovations in Engineering and Technology creative common licence This work is licensed under Creative common Attribution Non Commercial 4.0 Internation Licence
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