• Journal of KIISE
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• v.41 no.12
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• pp.1035-1049
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• 2014

This paper presents a new method to detect attack patterns in security-critical systems, based on a new notion of Behavior Ontology. Generally security-critical systems are large and complex, and they are subject to be attacked in every possible way. Therefore it is very complicated to detect various attacks through a semantic structure designed to detect such attacks. This paper handles the complication with Behavior Ontology, where patterns of attacks in the systems are defined as a sequences of actions on the class ontology of the systems. We define the patterns of attacks as sequences of actions, and the attack patterns can then be abstracted in a hierarchical order, forming a lattice, based on the inclusion relations. Once the behavior ontology for the attack patterns is defined, the attacks in the target systems can be detected both semantically and hierarchically in the ontology structure. When compared to other attack models, the behavior ontology analysis proposed in this paper is found to be very effective and efficient in terms of time and space.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.10
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• pp.5062-5079
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• 2017

Malicious insider threats have increased recently, and methods of the threats are diversifying every day. These insider threats are becoming a significant problem in corporations and governments today. From a technology standpoint, detecting potential insider threats is difficult in early stage because it is unpredictable. In order to prevent insider threats in early stage, it is necessary to collect all of insiders' data which flow in network systems, and then analyze whether the data are potential threat or not. However, analyzing all of data makes us spend too much time and cost. In addition, we need a large repository in order to collect and manage these data. To resolve this problem, we develop an indicator-based behavior ontology (IB2O) that allows us to understand and interpret insiders' data packets, and then to detect potential threats in early stage in network systems including social networks and company networks. To show feasibility of the behavior ontology, we developed a prototype platform called Insider Threat Detecting Extractor (ITDE) for detecting potential insider threats in early stage based on the behavior ontology. Finally, we showed how the behavior ontology would help detect potential inside threats in network system. We expect that the behavior ontology will be able to contribute to detecting malicious insider threats in early stage.

• Journal of information and communication convergence engineering
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• v.21 no.4
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• pp.306-315
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• 2023

This study proposed an SCA advice system. It is an ontology-based recommender that provides advice on appropriate computer curricula based on the behavior of high school students. The three computer curricula at Chiang Mai Rajabhat University include computer science (CS), information technology (IT), and web programming and security (WEB). This study aims to design the ontology framework for an SCA advice system. The system considers three core ontologies: student, computer-curriculum, and advice. After analyzing student behaviors, the behavior types of CS, IT, and WEB were determined to be SB-2, SB-1, and SB-5, respectively. All subjects in these three curricula were analyzed and grouped into seven groups. Their curricula were synthesized in terms of basic skills, basic knowledge, and characteristics. Finally, advice results can be obtained by consolidating the curriculum nature of the CS, IT, and WEB curricula.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.7 no.6
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• pp.777-784
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• 2017

In the architectural space, the user's behavior is the most important factor in evaluating the quality of architecture. Normally, the evaluation of user behavioral performance was carried out after a building was completed. Recently, interest in and efforts at pre-simulation based on information technology are accelerating. However, since existing user simulation technology is concerned mainly with simply escaping from a large space, it is impossible to simulate the behavior of multiple users in an architectural space. The present study strives to develop a human-figured intelligent agent for advanced user simulation based on ontology. The main purpose of the study is to employ the intelligent behaviors of a NPC(Non-player Character) to infer the ontology of both spatial and user information. In this paper, we intend to integrate ontology inference technology into the virtual space. And also, this study suggest the ontology visualization technology which illustrate the ontology-based information and their change in the spatial information.

• The Journal of the Korea Contents Association
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• v.11 no.9
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• pp.63-75
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• 2011

A number of process algebras have been proposed to develop distributed mobile real-time systems: pi-Calculus, Mobile Ambients Calculus, Bigraph, etc. However, as the systems get large and complex, the algebras become less suitable for understanding the interactions and mobility of the processes of the systems due to the size and complexity. Therefore it is necessary to handle the size and complexity for systematic understanding of the systems. This paper handles the size and complexity with a method of abstraction on sequences of interactions and movements of processes in the systems, which can be further organized in the form of hierarchically structured lattices, namely, Prism. The theoretical principle of the abstraction is based on a new concept of Behavior Ontology, which is extended from Active ontology. Prism allows the systems to be analyzed in the perspective of the lattices in Prism, which are characterized by the hierarchically organized behavioral properties of the developing systems, for systematic understanding the systems. In this way, the complexity of the interactions and the movements can be handled systematically in the semantically and hierarchically organized structure of the behavior.

• The Journal of Society for e-Business Studies
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• v.12 no.3
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• pp.151-175
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• 2007

Until now, most semantic web service discovery research has been carried out using either Web Service Modeling Ontology (WSMO) or a profile of OWL-based Web Service ontology (OWL-S). However, such efforts have focused primarily on service name and input/output ontology. Thus, the internal information of a service has not been utilized, and queries regarding internal information such as 'Find book-selling services allowing payment after delivery' are not addressed. This study outlines the development of TM-S (Topic Maps for Service) ontology and TMS-QL (TM-S Query Language), two novel technologies that address the aforementioned issues in semantic web service discovery research. TM-S ontology describes the behavior of services using process information and consists of three sub-ontologies: process signature ontology, process structure ontology and process concept ontology. TMS-QL allows users to describe service discovery requests.

• Journal of KIISE
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• v.44 no.11
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• pp.1178-1193
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• 2017

It is very important to understand system behaviors in collective pattern for each knowledge domain. However, there are structural limitations to represent collective behaviors because of the size of system components and the complexity of their interactions, causing the state explosion problem. Further composition with other systems is mostly impractical because of exponential growth of their size and complexity. This paper presents a practical method to model the collective behaviors, based on a new concept of domain engineering: behavior ontology. Firstly, the ontology defines each collective behavior of a system from active ontology. Secondly, the behaviors are formed in a quantifiably abstract lattice, called common regular expression. Thirdly, a lattice can be composed with other lattices based on quantifiably common elements. The method can be one of the most innovative approaches in representing system behaviors in collective pattern, as well as in minimization of system states to reduce system complexity. For implementation, a prototype tool, called PRISM, has been developed on ADOxx Meta-Modelling Platform.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.9 no.11
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• pp.1133-1144
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• 2019

Recently, the amount of image on the Internet has rapidly increased, due to the advancement of smart devices and various approaches to effective image retrieval have been researched under these situation. Existing image retrieval methods simply detect the objects in a image and carry out image retrieval based on the label of each object. Therefore, the semantic gap occurs between the image desired by a user and the image obtained from the retrieval result. To reduce the semantic gap in image retrievals, we connect the module for multiple objects classification based on deep learning with the module for human behavior classification. And we combine the connected modules with a behavior ontology. That is to say, we propose an image retrieval system considering the relationship between objects by using the combination of deep learning and behavior ontology. We analyzed the experiment results using walking and running data to take into account dynamic behaviors in images. The proposed method can be extended to the study of automatic annotation generation of images that can improve the accuracy of image retrieval results.

• Korean Journal of Computational Design and Engineering
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• v.16 no.3
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• pp.167-177
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• 2011

This paper describes an ontology-based intelligent CAD system for injection mold design, which has been developed based on a commercial CAD system called Unigraphics and an ontological framework for representing the implicit design knowledge as well as the explicit based on the extended function-behavior-structure (FBS) engineering design model that includes the constraint. The system also provides various convenient solid modeling capabilities for mold design and the design process modeling capability that facilitates mold redesign process.

• Journal of the Korea Society of Computer and Information
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• v.20 no.1
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• pp.29-37
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• 2015

Nowadays, the user-centric application based web 2.0 has replaced the web 1.0. The users gain and provide information by interactive network applications. As a result, traditional approaches that only extract and analyze users' local document operating behavior and network browsing behavior to build the users' preference profile cannot fully reflect their interests. Therefore this paper proposed a preference analysis and indicating approach based on the users' communication information from MicroBlog, such as reading, forwarding and @ behavior, and using the improved PersonalRank method to analyze the importance of a user to other users in the network and based on the users' communication behavior to update the weight of the items in the user preference. Simulation result shows that our proposed method outperforms the ontology model, TREC model, and the category model in terms of 11SPR value.