title: Detecting and classifying method based on similarity matching of Android malware behavior with profile.
abstract: Mass-market mobile security threats have increased recently due to the growth of mobile technologies and the popularity of mobile devices. Accordingly, techniques have been introduced for identifying, classifying, and defending against mobile threats utilizing static, dynamic, on-device, and off-device techniques. Static techniques are easy to evade, while dynamic techniques are expensive. On-device techniques are evasion, while off-device techniques need being always online. To address some of those shortcomings, we introduce Andro-profiler, a hybrid behavior based analysis and classification system for mobile malware. Andro-profiler main goals are efficiency, scalability, and accuracy. For that, Andro-profiler classifies malware by exploiting the behavior profiling extracted from the integrated system logs including system calls. Andro-profiler executes a malicious application on an emulator in order to generate the integrated system logs, and creates human-readable behavior profiles by analyzing the integrated system logs. By comparing the behavior profile of malicious application with representative behavior profile for each malware family using a weighted similarity matching technique, Andro-profiler detects and classifies it into malware families. The experiment results demonstrate that Andro-profiler is scalable, performs well in detecting and classifying malware with accuracy greater than 98 %, outperforms the existing state-of-the-art work, and is capable of identifying 0-day mobile malware samples.
authors.affiliation: Graduate School of Information Security, Korea University, Seoul, Republic of Korea.
authors.forename: Jae-Wook
authors.initials: JW
authors.lastname: Jang
authors.affiliation: Graduate School of Information Security, Korea University, Seoul, Republic of Korea.
authors.forename: Jaesung
authors.initials: J
authors.lastname: Yun
authors.affiliation: Computer Science and Engineering Department, State University of New York at Buffalo (SUNY Buffalo), Buffalo, NY USA.
authors.forename: Aziz
authors.initials: A
authors.lastname: Mohaisen
authors.affiliation: Graduate School of Information Security, Korea University, Seoul, Republic of Korea.
authors.forename: Jiyoung
authors.initials: J
authors.lastname: Woo
authors.affiliation: Graduate School of Information Security, Korea University, Seoul, Republic of Korea.
authors.forename: Huy Kang
authors.initials: HK
authors.lastname: Kim
keywords: Android; Behavior profiling; Malware; Similarity; System call