Risk measurement method for privilege escalation attacks on android apps based on process algebra

被引：0

作者：

Shen, Limin ^{[1
]}

Li, Hui ^{[1
,2
]}

Wang, Hongyi ^{[3
]}

Wang, Yihuan ^{[1
]}

Feng, Jiayin ^{[1
,4
]}

Jian, Yuqing ^{[1
,4
]}

机构：

[1] School of Information Science and Engineering, Yanshan University, Qinhuangdao,066004, China

[2] School of Business Administration, Hebei Normal University of Science and Technology, Qinhuangdao,066004, China

[3] Department of Commerce and Trade, Qinhuangdao Vocational and Technical College, Qinhuangdao,066100, China

[4] School of Mathematics and Information Science and Technology, Hebei Normal University of Science and Technology, Qinhuangdao,066004, China

来源：

Information (Switzerland) | 2020年 / 11卷 / 06期

基金：

中国国家自然科学基金;

关键词：

Algebra - Benchmarking - Functions - Classification (of information) - Risk assessment - Sensitive data;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

On the Android platform, information leakage can use an application-layer privilege escalation attack composed of multi-app collusion. However, the detection effect of a single app that can construct privilege escalation attacks is not good. Furthermore, the existing software and app measurement methods are not applicable to the measurement of collusion privilege escalation attacks. We propose a method for measuring the risk of a single app by using process algebra to model and determine the attack behavior, and we construct a measurement function based on sensitive data transitions and the feature set of attack behavior. Through the analysis of the privilege escalation attack model, the feature set of attack behavior is obtained. Then, based on the extracted behavior feature set, process algebra is used to model the dangerous behavior of an app. The dangerous behavior of the app is determined by weak equivalence and non-equivalence, and finally the risk of the app is measured based on the measurement function. Three known applications are used to verify the attack, and the risk measurement values are above 0.98. Based on the classification of applications on the market, we select typical apps in each category to build the test set. Benchmark tests and test set experiments show that the risk measurement results are consistent with the actual detection results, verifying the feasibility and effectiveness of this method. © 2020 by the authors.

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