Active semi-supervised approach for checking app behavior against its description

• Main Authors: MA SIQI, WANG, Shaowei, David LO, DENG, Robert H., SUN, Cong
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2015
• Subjects: App Mining; Malware Detection; Deviant Behavior Detection; Text Mining; Classification; Computer Sciences; Information Security; Software Engineering
• Online Access: https://ink.library.smu.edu.sg/sis_research/2885; https://ink.library.smu.edu.sg/context/sis_research/article/3885/viewcontent/compsac15_malware.pdf
• Institution: Singapore Management University

Mobile applications are popular in recent years. They are often allowed to access and modify users' sensitive data. However, many mobile applications are malwares that inappropriately use these sensitive data. To detect these malwares, Gorla et al. Propose CHABADA which compares app behaviors a...