Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

11-2019

Abstract

Currently, Android malware detection is mostly performed on the server side against the increasing number of Android malware. Powerful computing resource gives more exhaustive protection for Android markets than maintaining detection by a single user in many cases. However, apart from the Android apps provided by the official market (i.e., Google Play Store), apps from unofficial markets and third-party resources are always causing a serious security threat to end-users. Meanwhile, it is a time-consuming task if the app is downloaded first and then uploaded to the server side for detection because the network transmission has a lot of overhead. In addition, the uploading process also suffers from the threat of attackers. Consequently, a last line of defense on Android devices is necessary and much-needed. To address these problems, in this paper, we propose an effective Android malware detection system, MobiDroid, leveraging deep learning to provide a real-time secure and fast response environment on Android devices. Although a deep learning-based approach can be maintained on server side efficiently for detecting Android malware, deep learning models cannot be directly deployed and executed on Android devices due to various performance limitations such as computation power, memory size, and energy. Therefore, we evaluate and investigate the different performances with various feature categories, and further provide an effective solution to detect malware on Android devices. The proposed detection system on Android devices in this paper can serve as a starting point for further study of this important area.

Keywords

Android malware, Malware detection, Deep neural network, Mobile platform

Discipline

OS and Networks | Software Engineering

Research Areas

Software and Cyber-Physical Systems

Publication

Proceedings of the 24th International Conference on Engineering of Complex Computer Systems, Guangzhou, China, 2019 November 10-13

First Page

61

Last Page

70

ISBN

9781728146461

Identifier

10.1109/ICECCS.2019.00014

Publisher

IEEE

City or Country

Guangzhou, China

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