Multilevel permission extraction in android applications for malware detection

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

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

1 Citation (Scopus)

Abstract

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%.

Original languageEnglish
Title of host publicationCITS 2019 - Proceeding of the 2019 International Conference on Computer, Information and Telecommunication Systems
EditorsMohammad S. Obaidat, Zhenqiang Mi, Kuei-Fang Hsiao, Petros Nicopolitidis, Daniel Cascado-Caballero
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538640883
DOIs
Publication statusPublished - Aug 2019
Event2019 International Conference on Computer, Information and Telecommunication Systems, CITS 2019 - Beijing, China
Duration: Aug 28 2019Aug 31 2019

Publication series

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

Conference

Conference2019 International Conference on Computer, Information and Telecommunication Systems, CITS 2019
CountryChina
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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