Detecting Android Malware Based on Extreme Learning Machine

Yuxia Sun, Yunlong Xie, Zhi Qiu, Yuchang Pan, Jian Weng, Song Guo

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

8 Citations (Scopus)

Abstract

To relieve increasingly prominent security issues of Android applications, static malware-detection techniques have become essential, due to their rapid and convenient detection processes which do not require running the detected applications. Most of current commercial anti-malware tools utilize signatures of known malicious Android codes for static detection, but are unable to find out unknown, especially newly created, malware. Many existing malware-detection researches rely on traditional machine learning techniques to analyze some static features of Android applications such as permissions and API calls, but the detection approaches still have room for improvement with respect to simplicity, effectiveness or efficiency. To overcome the limitations of the above detection techniques, we propose a novel static approach to detect malicious Android applications by proposing a set of Android program features, consisting of sensitive permissions and sensitive API calls, and by utilizing Extreme Learning Machine. We implemented our approach with an automated testing tool called WaffleDetector. Controlled experiments have been conducted to compare our approach and the existing ones on detecting malicious Android applications, and the results show that our approach excels the existing ones with minimal human intervention, better detection effectiveness and less detection time.

Original languageEnglish
Title of host publicationProceedings - 2017 IEEE 15th International Conference on Dependable, Autonomic and Secure Computing, 2017 IEEE 15th International Conference on Pervasive Intelligence and Computing, 2017 IEEE 3rd International Conference on Big Data Intelligence and Computing and 2017 IEEE Cyber Science and Technology Congress, DASC-PICom-DataCom-CyberSciTec 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages47-53
Number of pages7
ISBN (Electronic)9781538619551
DOIs
Publication statusPublished - 29 Mar 2018
Event15th IEEE International Conference on Dependable, Autonomic and Secure Computing, 2017 IEEE 15th International Conference on Pervasive Intelligence and Computing, 2017 IEEE 3rd International Conference on Big Data Intelligence and Computing and 2017 IEEE Cyber Science and Technology Congress, DASC-PICom-DataCom-CyberSciTec 2017 - Orlando, United States
Duration: 6 Nov 201711 Nov 2017

Publication series

NameProceedings - 2017 IEEE 15th International Conference on Dependable, Autonomic and Secure Computing, 2017 IEEE 15th International Conference on Pervasive Intelligence and Computing, 2017 IEEE 3rd International Conference on Big Data Intelligence and Computing and 2017 IEEE Cyber Science and Technology Congress, DASC-PICom-DataCom-CyberSciTec 2017
Volume2018-January

Conference

Conference15th IEEE International Conference on Dependable, Autonomic and Secure Computing, 2017 IEEE 15th International Conference on Pervasive Intelligence and Computing, 2017 IEEE 3rd International Conference on Big Data Intelligence and Computing and 2017 IEEE Cyber Science and Technology Congress, DASC-PICom-DataCom-CyberSciTec 2017
Country/TerritoryUnited States
CityOrlando
Period6/11/1711/11/17

Keywords

  • Android
  • Extreme Learning Mchine
  • Malware Detection
  • Sensitive API
  • Sensitive Permission

ASJC Scopus subject areas

  • Safety, Risk, Reliability and Quality
  • Health Informatics
  • Artificial Intelligence
  • Computer Networks and Communications
  • Hardware and Architecture
  • Computer Science Applications
  • Information Systems

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