Android Malware Detection with Deep Learning using RNN from Opcode Sequences

Android Malware Detection with Deep Learning using RNN from Opcode Sequences

Android

Malware

Opcodes

Recurrent Neural Networks

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.

Lakshmanarao, A.

Shashi, M.

International Journal of Interactive Mobile Technologies (iJIM); Vol. 16 No. 01 (2022); pp. 145-157

1865-7923

International Association of Online Engineering (IAOE), Vienna, Austria

2022-01-18

Copyright (c) 2021 A Lakshmanarao, M Shashi

https://creativecommons.org/licenses/by/4.0

application/pdf

eng

info:eu-repo/semantics/article

info:eu-repo/semantics/publishedVersion

Peer-reviewed Article