ontology inference
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2018 ◽  
Vol 2018 ◽  
pp. 1-13
Author(s):  
Jian Jiao ◽  
Qiyuan Liu ◽  
Xin Chen ◽  
Hongsheng Cao

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.


2017 ◽  
Vol 68 ◽  
pp. 491-499 ◽  
Author(s):  
Rouaa Wannous ◽  
Jamal Malki ◽  
Alain Bouju ◽  
Cécile Vincent

2014 ◽  
Vol 9 (5) ◽  
Author(s):  
Qiang Ge ◽  
Guohua Shen ◽  
Zhiqiu Huang ◽  
Changbo Ke

Author(s):  
Song-il Cha ◽  
Z. M. Ma

Web-tables are ubiquitous in Web pages. Since tables themselves are organized structurally and semantically, they are good resources from which we can easily extract ontology. But, most Web-tables are designed for intuitive perception of humans, thus, it has a certain limit to interpret table content using only structural information of the table. So this paper focuses on the method for interpretation of table content based on semantic characteristics of the table. In order to obtain many property elements used for ontology inference, in this paper, the authors discuss how to extract ontology properties from Web-tables. The extracted properties include the following elements: Is-a relationship, class-instance relationship, triple, property domain, property range, symmetric property, transitive property, functional property, and inverse functional property, property for defining super-sub relationship. Through experiment, the authors show that their method can effectively extract property elements from Web-tables.


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