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RISS 인기검색어
- 검색키워드
- 저자 : Somjit Arch-int (검색결과 3 건)
- 무료
- 기관 내 무료
- 유료
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Ontology Mapping and Rule-Based Inference for Learning Resource Integration
Jetinai, Kotchakorn,Arch-int, Ngamnij,Arch-int, Somjit The Korea Institute of Information and Commucation 2016 Journal of information and communication convergen Vol.14 No.2
With the increasing demand for interoperability among existing learning resource systems in order to enable the sharing of learning resources, such resources need to be annotated with ontologies that use different metadata standards. These different ontologies must be reconciled through ontology mediation, so as to cope with information heterogeneity problems, such as semantic and structural conflicts. In this paper, we propose an ontology-mapping technique using Semantic Web Rule Language (SWRL) to generate semantic mapping rules that integrate learning resources from different systems and that cope with semantic and structural conflicts. Reasoning rules are defined to support a semantic search for heterogeneous learning resources, which are deduced by rule-based inference. Experimental results demonstrate that the proposed approach enables the integration of learning resources originating from multiple sources and helps users to search across heterogeneous learning resource systems.
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An Adaptive Multi-Layer Block Data-Hiding Algorithm that uses Edge Areas of Gray-Scale Images
Tuan Duc Nguyen,Somjit Arch-int,Ngamnij Arch-int 보안공학연구지원센터 2015 International Journal of Security and Its Applicat Vol.9 No.6
Embedding data into smooth regions introduces stego-images with poor security and visual quality. Edge adaptive steganography, in which the flat regions are not employed to carry a message at low embedding rates, was proposed. However, for the high embedding rates, smooth regions are contaminated to hide a secret message. In this paper, we present an adaptive multi-layer block data-hiding (MBDH) algorithm, in which the embedding regions are adaptively selected according to the number of the secret message bits and the texture characteristic of a cover-image. Via employing the MBDH algorithm, more secret message bits are embedded into the sharp regions. Therefore, the smooth regions are not used, even at high embedding rates. Furthermore, most of edge adaptive steganography algorithms have a limited capacity when the smooth regions are not employed in data hiding. The proposed scheme solves this issue when it can embed more secret bits into the selected regions while the perceptual quality of stego-images is still maintained. The experimental results were evaluated on 10,000 natural gray-scale images. The visual attack, targeted steganalysis, and universal steganalysis are employed to examine the performance of the proposed scheme. The results show that the new scheme significantly overcomes the previous edge-based approaches and least significant bit (LSB) based methods in term of security and visual quality.
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A Novel Lightweight Hybrid Intrusion Detection Method Using a Combination of Data Mining Techniques
Jatuphum Juanchaiyaphum,Ngamnij Arch-int,Somjit Arch-int,Saiyan Saiyod 보안공학연구지원센터 2015 International Journal of Security and Its Applicat Vol.9 No.4
Hybrid intrusion detection systems that make use of data mining techniques, in order to improve effectiveness, have been actively pursued in the last decade. However, their complexity to build detection models has become very expensive when confronted with large-scale datasets, making them unviable for real-time retraining. In order to overcome the limitation of the conventional hybrid method, we propose a new lightweight hybrid intrusion detection method that consists of a combination of feature selection, clustering and classification. According to our hypothesis that there are different natures of attack events in each of network protocols, the proposed method examines each of network protocol data separately, but their processes are the same. First, the training dataset is divided into training subsets, depending on their type of network protocol. Next, each training subset is reduced dimensionally by eliminating the irrelevant and redundant features throughout the feature selection process; and then broken down into disjointed regions, depending on their similar feature values, by K -Means clustering. Lastly, the C4.5 decision tree is used to build multiple misuse detection models for suspicious regions, which deviate from the normal and anomaly regions. As a result, each detection model is built from high-quality data, which are less complex and consist of relevant data. For better understanding of the enhanced performance, the proposed method was evaluated through experiments using the NSL-KDD dataset. The experimental results indicate that the proposed method is better in terms of effectiveness (F-value: 0.9957, classification accuracy: 99.52%, false positive rate: 0.26%), and efficiency (the training and testing times of the proposed method are approximately 33% and 25%, respectively, of the time required for its comparison) than the conventional hybrid method using the same algorithm.
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