Multilevel permission extraction in android applications for malware detection

Zhen Wang, Kai Li, Yan Hu, Akira Fukuda, Weiqiang Kong

研究成果: Chapter in Book/Report/Conference proceedingConference contribution

1 被引用数 (Scopus)

抄録

With the widespread use of Android applications in security-sensitive scenarios, more and more Android malware has been discovered. Existing work on malware detection fail to automatically learn effective feature interactions, which are, however, the key to the success of many prediction models. In order to detect malware efficiently and accurately, in this paper, we propose Multilevel Permission Extraction, an approach to automatically identify permission interactions that are effective in distinguishing between malicious and benign applications. We then utilize the extracted information to classify malicious and benign applications by machine learning based classification algorithms. We evaluate our approach in a large data set consisting of 4,868 benign applications and 4,868 malicious applications. The experimental results show that our malware detection approach can achieve over 95.8% in accuracy, precision, recall, and F-Score. Compared with two state-of-the-art approaches, we can achieve a better malware detection rate of 97.88%.

本文言語英語
ホスト出版物のタイトルCITS 2019 - Proceeding of the 2019 International Conference on Computer, Information and Telecommunication Systems
編集者Mohammad S. Obaidat, Zhenqiang Mi, Kuei-Fang Hsiao, Petros Nicopolitidis, Daniel Cascado-Caballero
出版社Institute of Electrical and Electronics Engineers Inc.
ISBN(電子版)9781538640883
DOI
出版ステータス出版済み - 8 2019
イベント2019 International Conference on Computer, Information and Telecommunication Systems, CITS 2019 - Beijing, 中国
継続期間: 8 28 20198 31 2019

出版物シリーズ

名前CITS 2019 - Proceeding of the 2019 International Conference on Computer, Information and Telecommunication Systems

会議

会議2019 International Conference on Computer, Information and Telecommunication Systems, CITS 2019
Country中国
CityBeijing
Period8/28/198/31/19

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition
  • Information Systems
  • Information Systems and Management
  • Safety, Risk, Reliability and Quality
  • Computer Networks and Communications
  • Hardware and Architecture

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