NDLI: Attack Tree Based Android Malware Detection with Hybrid Analysis

Content Provider	IEEE Xplore Digital Library
Author	Shuai Zhao Xiaohong Li Guangquan Xu Lei Zhang Zhiyong Feng
Copyright Year	2014
Description	Author affiliation: Tianjin Key Lab. of Cognitive Comput. & Applic., Tianjin Univ., Tianjin, China (Shuai Zhao; Xiaohong Li; Guangquan Xu; Lei Zhang; Zhiyong Feng)
Abstract	This paper proposes an Android malware detection approach based on attack tree. Attack tree model is extended to provide a novel way to organize and exploit behavior rules. Connections between attack goals and application capability are represented by an attack tree structure and behavior rules are assigned to every attack path in the attack tree. In this way, fine-grained and comprehensive static capability estimation and dynamic behavior detection can be achieved. This approach employs a hybrid static-dynamic analysis method. Static analysis tags attack tree nodes based on application capability. It filters the obviously benign applications and highlights the potential attacks in suspicious ones. Dynamic analysis selects rules corresponding to the capability and conducts detection according to runtime behaviors. In dynamic analysis, events are simulated to trigger behaviors based on application components, and hence it achieves high code coverage. Finally, in this way, we implement an automatic malware detection prototype system called AM Detector. The experiment result shows that the true positive rate is 88.14% and the false positive rate is as low as 1.80%.
Starting Page	380
Ending Page	387
File Size	252414
Page Count	8
File Format	PDF
ISBN	9781479965137
DOI	10.1109/TrustCom.2014.49
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-09-24
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Privacy Runtime Attack tree Humanoid robots Vegetation Feature extraction Hybrid analysis Malware Androids Detection Android
Content Type	Text
Resource Type	Article

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