Android Malware Detection with Deep Learning using RNN from Opcode Sequences

Authors

  • A. Lakshmanarao Aditya Engineering College,Surampalem
  • M. Shashi Andhra University, Visakhapatnam

DOI:

https://doi.org/10.3991/ijim.v16i01.26433

Keywords:

Android, Malware, Opcodes, Recurrent Neural Networks

Abstract


Android is the most widely used operating system in smartphones. Mobile users can download and access apps easily from the play store. Due to lack of security awareness and risk associated with mobile apps, malware apps would be downloaded by normal users in general. The consequences after installing a malware app are unpredictable. Malware apps can gather user personal data, browsing history, user profiles, user sensitive data like passwords. Hence, android malware detection is essential for providing security to mobile users. Android malware detection using machine learning is done either by extracting static features (opcodes, permissions, intents, system commands) or by extracting dynamic features (log behavior, system calls, dataflow). In this paper, opcode sequences are extracted from malware and benign apps, and Recurrent Neural Networks are proposed on extracted sequences. Benign apps are collected from the play store, apkpure.com and malware apps are collected from the virus share website. The proposed Recurrent Neural Network model could achieve 96% accuracy for android malware detection.

Author Biographies

A. Lakshmanarao, Aditya Engineering College,Surampalem

Department of It

M. Shashi, Andhra University, Visakhapatnam

Department of CS&SE

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Published

2022-01-18

How to Cite

Lakshmanarao, A., & Shashi, M. (2022). Android Malware Detection with Deep Learning using RNN from Opcode Sequences. International Journal of Interactive Mobile Technologies (iJIM), 16(01), pp. 145–157. https://doi.org/10.3991/ijim.v16i01.26433

Issue

Section

Papers