MobiDroid: A performance-sensitive malware detection system on mobile platform

Ruitao Feng, Sen Chen, Xiaofei Xie, Lei Ma, Guozhu Meng, Yang Liu, Shang Wei Lin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationProceedings - 2019 24th International Conference on Engineering of Complex Computer Systems, ICECCS 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages61-70
Number of pages10
ISBN (Electronic)9781728146461
DOIs
Publication statusPublished - Nov 2019
Event24th International Conference on Engineering of Complex Computer Systems, ICECCS 2019 - Guangzhou, China
Duration: Nov 10 2019Nov 13 2019

Publication series

NameProceedings of the IEEE International Conference on Engineering of Complex Computer Systems, ICECCS
Volume2019-November

Conference

Conference24th International Conference on Engineering of Complex Computer Systems, ICECCS 2019
CountryChina
CityGuangzhou
Period11/10/1911/13/19

All Science Journal Classification (ASJC) codes

  • Hardware and Architecture
  • Computer Networks and Communications

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