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Journal of Electrical and Computer Engineering
Volume 2018, Article ID 9250297, 13 pages
Research Article

Behavior Intention Derivation of Android Malware Using Ontology Inference

1Beijing Key Laboratory of Internet Culture and Digital Dissemination Research, Beijing Information Science and Technology University, Beijing, China
2School of Computer Science, Beijing Information Science and Technology University, Beijing, China

Correspondence should be addressed to Jian Jiao; nc.ude.utsib@naijoaij

Received 2 November 2017; Revised 26 January 2018; Accepted 20 February 2018; Published 1 April 2018

Academic Editor: Ahmad K. Malik

Copyright © 2018 Jian Jiao et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Previous researches on Android malware mainly focus on malware detection, and malware’s evolution makes the process face certain hysteresis. The information presented by these detected results (malice judgment, family classification, and behavior characterization) is limited for analysts. Therefore, a method is needed to restore the intention of malware, which reflects the relation between multiple behaviors of complex malware and its ultimate purpose. This paper proposes a novel description and derivation model of Android malware intention based on the theory of intention and malware reverse engineering. This approach creates ontology for malware intention to model the semantic relation between behaviors and its objects and automates the process of intention derivation by using SWRL rules transformed from intention model and Jess inference engine. Experiments on 75 typical samples show that the inference system can perform derivation of malware intention effectively, and 89.3% of the inference results are consistent with artificial analysis, which proves the feasibility and effectiveness of our theory and inference system.