Shinjin Kang¹, Soo Kyun Kim^2,*

CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3725-3736, 2021, DOI:10.32604/cmc.2021.016828

Abstract In this study, we propose a low-cost system that can detect the space outlier utilization of residents in an indoor environment. We focus on the users’ app usage to analyze unusual behavior, especially in indoor spaces. This is reflected in the behavioral analysis in that the frequency of using smartphones in personal spaces has recently increased. Our system facilitates autonomous data collection from mobile app logs and Google app servers and generates a high-dimensional dataset that can detect outlier behaviors. The density-based spatial clustering of applications with noise (DBSCAN) algorithm was applied for effective singular movement analysis. To analyze high-level… More >

Tianjun Wu, Yuexiang Yang

Intelligent Automation & Soft Computing, Vol.25, No.4, pp. 755-766, 2019, DOI:10.31209/2019.100000079

Abstract While many research efforts have been around auditing individual android apps, the security issues related to the interaction among multiple apps are less studied. Due to the hidden nature of Inter-App communications, few existing security tools are able to detect such related vulnerable behaviors. This paper proposes to perform overall security auditing using dynamic analysis techniques. We focus on data leakage as it is one of the most common vulnerabilities for Android applications. We present an app auditing system AppWalker, which uses concolic execution on a set of apps. We use static Inter-App taint analysis to guide the dynamic auditing… More >