De-LADY: Deep learning based Android malware detection using Dynamic features – Journal of Internet Services and Information Security

Volume 11 - Issue 2

De-LADY: Deep learning based Android malware detection using Dynamic features

Vikas Sihag Sardar Patel University of Police, Security and Criminal Justice, Jodhpur, India, National Institute of Technology, Raipur, India
vikas.sihag@policeuniversity.ac.in
Manu Vardhan National Institute of Technology, Raipur, India
mvardhan.cs@nitrr.ac.in
Pradeep Singh National Institute of Technology, Raipur, India
psingh.cs@nitrr.ac.in
Gaurav Choudhary School of Computing Science and Engineering ,VIT Bhopal University, Bhopal, Madhya Pradesh
gauravchoudhary7777@gmail.com
Seiil Son Korea Communication Agency, South Korea
seiilson@kca.kr

DOI: 10.22667/JISIS.2021.05.31.034

Keywords: Android, Malware detection, Code obfuscation, Familial classification

Abstract

Popularity and market share of Android operating system has given significant rise to malicious apps targeting it. Traditional malware detection methods are obsolete as current malwares are equipped with state of the art obfuscation methods to hide their intent from scanning engines. In this paper, we propose De-LADY (Deep Learning based Android malware detection using DYnamic features) an obfuscation resilient approach. It utilizes behavioral characteristics from dynamic analysis of an application executed in emulated environment. The proposed approach is evaluated against 13533 applications from categories such as banking, gaming and utilities. De-LADY is effective with 98.08% detection rate and 98.84% F-measure. Furthermore, it outperformed existing machine learning approaches

Date

May 2021

Page Number

34-45