Android Application Risk Indicator Based on Feature Analysis Utilizing Machine Learning

Hyochang Baek, Minhee Joo, Won Park, Youngin You, Kyung Ho Lee

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

Abstract

As the penetration rate of smart mobile devices has increased, threats targeting the Android platform, which accounts for the majority of mobile operating systems, have increased. As a typical example, a fake Korea Financial Supervisory Service application(app) appeared at the end of 2017. When users installed this app and called the Financial Supervisory Service, there was a case of fake loan consultation, which resulted in financial loss and leakage of personal information. There have been a variety of malicious apps targeting mobile devices. As a result, it became necessary to detect the risks to such malicious apps and to make decisions about the apps. In this paper, we created a model to evaluate the risk of apps in Android and define the characteristics of each element. In addition, the risk from the model is used to make a risk map for decision making using unsupervised algorithms. To make the risk map in this paper uses the data of 2970 apps that is malicious or benign. As a result of the experiment, some of the benign apps were classified as very high risk. They had a lot of high-risk permissions, and there was a need for users to be careful. The results of this study can help users know the exact risk of Android apps and help detect unknown malicious apps.

Original languageEnglish
Title of host publication2019 International Conference on Platform Technology and Service, PlatCon 2019 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728112886
DOIs
Publication statusPublished - 2019 Mar 18
Event6th International Conference on Platform Technology and Service, PlatCon 2019 - Jeju, Korea, Republic of
Duration: 2019 Jan 282019 Jan 30

Publication series

Name2019 International Conference on Platform Technology and Service, PlatCon 2019 - Proceedings

Conference

Conference6th International Conference on Platform Technology and Service, PlatCon 2019
CountryKorea, Republic of
CityJeju
Period19/1/2819/1/30

Keywords

  • Android application
  • FAIR model
  • Risk assessment

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Information Systems
  • Software
  • Computer Networks and Communications

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  • Cite this

    Baek, H., Joo, M., Park, W., You, Y., & Lee, K. H. (2019). Android Application Risk Indicator Based on Feature Analysis Utilizing Machine Learning. In 2019 International Conference on Platform Technology and Service, PlatCon 2019 - Proceedings [8669424] (2019 International Conference on Platform Technology and Service, PlatCon 2019 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/PlatCon.2019.8669424