Android Application Risk Indicator Based on Feature Analysis Utilizing Machine Learning

Hyochang Baek; Minhee Joo; Won Park; Youngin You; Kyungho Lee

doi:10.1109/PlatCon.2019.8669424

Android Application Risk Indicator Based on Feature Analysis Utilizing Machine Learning

Hyochang Baek
, Minhee Joo
, Won Park
, Youngin You
, Kyungho Lee

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

3 Citations (Scopus)

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
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	https://doi.org/10.1109/PlatCon.2019.8669424
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

Conference

Conference	6th International Conference on Platform Technology and Service, PlatCon 2019
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

Access to Document

10.1109/PlatCon.2019.8669424

Cite this

Baek, H., Joo, M., Park, W., You, Y., & Lee, K. (2019). Android Application Risk Indicator Based on Feature Analysis Utilizing Machine Learning. In 2019 International Conference on Platform Technology and Service, PlatCon 2019 - Proceedings Article 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

Android Application Risk Indicator Based on Feature Analysis Utilizing Machine Learning. / Baek, Hyochang; Joo, Minhee; Park, Won et al.
2019 International Conference on Platform Technology and Service, PlatCon 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2019. 8669424 (2019 International Conference on Platform Technology and Service, PlatCon 2019 - Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Baek, H, Joo, M, Park, W, You, Y & Lee, K 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., 6th International Conference on Platform Technology and Service, PlatCon 2019, Jeju, Korea, Republic of, 19/1/28. https://doi.org/10.1109/PlatCon.2019.8669424

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

Baek, Hyochang ; Joo, Minhee ; Park, Won et al. / Android Application Risk Indicator Based on Feature Analysis Utilizing Machine Learning. 2019 International Conference on Platform Technology and Service, PlatCon 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2019. (2019 International Conference on Platform Technology and Service, PlatCon 2019 - Proceedings).

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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.",

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AB - 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.

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Android Application Risk Indicator Based on Feature Analysis Utilizing Machine Learning

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this