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 language | English |
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Title of host publication | 2019 International Conference on Platform Technology and Service, PlatCon 2019 - Proceedings |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
ISBN (Electronic) | 9781728112886 |
DOIs | |
Publication status | Published - 2019 Mar 18 |
Event | 6th International Conference on Platform Technology and Service, PlatCon 2019 - Jeju, Korea, Republic of Duration: 2019 Jan 28 → 2019 Jan 30 |
Publication series
Name | 2019 International Conference on Platform Technology and Service, PlatCon 2019 - Proceedings |
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Conference
Conference | 6th International Conference on Platform Technology and Service, PlatCon 2019 |
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Country/Territory | Korea, Republic of |
City | Jeju |
Period | 19/1/28 → 19/1/30 |
Bibliographical note
Funding Information:This work was supported by the Institute for Information communications Technology Promotion(IITP) grant funded by the Korea government(MSIT) (No.2017-0-01853, Machine Learning based Intelligent Malware Analysis Platform)
Publisher Copyright:
© 2019 IEEE.
Keywords
- Android application
- FAIR model
- Risk assessment
ASJC Scopus subject areas
- Human-Computer Interaction
- Information Systems
- Software
- Computer Networks and Communications