EasyDefense: Towards Easy and Effective Protection Against Malware for Smartphones

Bingfei Ren, Chuanchang Liu, Bo Cheng, Yimeng Feng, Junliang Chen

Research output: Chapter in Book/Report/Conference proceedingConference abstractpeer-review

2 Citations (Scopus)

Abstract

As the dominant mobile operating system in the markets of smartphones, Android platform is increasingly targeted by attackers. Besides, attackers often produce novel malware to bypass the conventional detection approaches, which are largely reliant on expert analysis to design the discriminative features manually. Therefore, more effective and easy-to-use approaches for detection of Android malware are in demand. In this paper, we design and implement EasyDefense, a lightweight defense system that is integrated with Android OS for easy and effective detection of Android malware utilizing machine learning methods and the ensemble of them. Besides universal static features such as permissions and API calls, EasyDefense also employs the N-gram features of operation codes (opcodes). These N-gram features are extracted and learnt automatically from raw data of applications. Experimental results on 204,650 applications show that users can easily and effectively protect the privacy and security on their smartphones through this system.
Original languageEnglish
Title of host publicationMobiCom '17
Subtitle of host publicationProceedings of the 23rd Annual International Conference on Mobile Computing and Networking
Place of PublicationNew York, NY
PublisherAssociation for Computing Machinery (ACM)
Pages570-572
Number of pages3
ISBN (Print)9781450349161
DOIs
Publication statusPublished - 4 Oct 2017
Externally publishedYes

Keywords

  • Security
  • Privacy
  • N-gram
  • Machine learning

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