• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.12
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• pp.2287-2297
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• 2007

The proposed ANIDS(Advanced Network Intrusion Detection System) which is network-based IDS using Association Rule Mining, collects the packets on the network, analyze the associations of the packets, generates the pattern graph by using the highly associated packets using Association Rule Mining, and detects the intrusion by using the generated pattern graph. ANIDS consists of PMM(Packet Management Module) collecting and managing packets, PGGM(Pattern Graph Generate Module) generating pattern graphs, and IDM(Intrusion Detection Module) detecting intrusions. Specially, PGGM finds the candidate packets of Association Rule large than $Sup_{min}$ using Apriori algorithm, measures the Confidence of Association Rule, and generates pattern graph of association rules large than $Conf_{min}$. ANIDS reduces the false positive by using pattern graph even before finalizing the new pattern graph, the pattern graph which is being generated is compared with the existing one stored in DB. If they are the same, we can estimate it is an intrusion. Therefore, this paper can reduce the speed of intrusion detection and the false positive and increase the detection ratio of intrusion.

• The Journal of Society for e-Business Studies
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• v.24 no.3
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• pp.87-98
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• 2019

As artificial intelligence is actively studied, it is being applied to various fields such as image, video and natural language processing. The natural language processing, in particular, is being studied to enable computers to understand the languages spoken and spoken by people and is considered one of the most important areas in artificial intelligence technology. In natural language processing, it is a complex, but important to make computers learn to understand a person's common sense and generate results based on the person's common sense. Knowledge graphs, which are linked using the relationship of words, have the advantage of being able to learn common sense easily from computers. However, the existing knowledge graphs are organized only by focusing on specific languages and fields and have limitations that cannot respond to neologisms. In this paper, we propose an intelligent chatbotsystem that collects and analyzed data in real time to build an automatically scalable knowledge graph and utilizes it as the base data. In particular, the fine-tuned BERT-based for relation extraction is to be applied to auto-growing graph to improve performance. And, we have developed a chatbot that can learn human common sense using auto-growing knowledge graph, it verifies the availability and performance of the knowledge graph.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.42 no.1
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• pp.27-34
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• 2005

In this paper, we propose the Q-edge labeling method related to the graph embedding problem in binomial trees. This result is able to design a new reliable interconnection networks with maximum connectivity using Q-edge labels as jump sequence of circulant graph. The circulant graph is a generalization of Harary graph which is a solution of the optimal problem to design a maximum connectivity graph consists of n vertices End e edgies. And this topology has optimal broadcasting because of having binomial trees as spanning tree.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.24 no.3
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• pp.437-444
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• 2014

In this paper, we introduce a Graph based Binary Code Execution Path Exploration Platform. In the graph, a node is defined as a conditional branch instruction, and an edge is defined as the other instructions. We implemented prototype of the proposed method and works well on real binary code. Experimental results show proposed method correctly explores execution path of target binary code. We expect our method can help Software Assurance, Secure Programming, and Malware Analysis more correct and efficient.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.22-24
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• 2021

This paper is a proposal for edge weight prediction using neural networks to graph configurations of nodes and edges. Brand is one of the components of society. and one of the brand's most important strategies is geographical location strategy. This paper is focus on that strategy. In This paper propose two things: 1) Graph Configuration. node consists of brand store, edge consists of store-to-store relationships and edge weight consists of actual walk and drive distance values. 2) numbering edges and training neural networks to predict next store distance values. It is expected to be useful in analyzing successful brand geographical location strategies.

• The KIPS Transactions:PartB
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• v.8B no.5
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• pp.533-538
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• 2001

In this paper, we extend FARGs single mode attribute to multiple attributes for real image application and present a new CBIR using FMARG(Fuzzy Multiple Attribute Relational Graph), which can handle queries involving multiple attributes, not only object label, but also color, texture and spatial relation. In the experiment using the synthetic image database of 1,024 images and the natural image database of 1.026 images built from NETRA database and Corel Draw, the proposed approach shows 6~30% recall increase in the synthetic image database and a good performance, at the displacements and the retrieved number of similar images in the natural image database, compared with the single attribute approach.

• Journal of the Korea Society of Computer and Information
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• v.4 no.4
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• pp.15-24
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• 1999

In this paper, we propose a new CPLD technology mapping algorithm improving run-time under time constraint. In our technology mapping algorithm, a given logic equation is constructed as the DAG type, then the DAG is reconstructed by replicating the node that outdegree is more than or equal to 2. As a result. it makes delay time and the number of CLBs, run-time to be minimized. Also, after the number of multi-level is defined and cost of each nodes is calculated, the graph is partitioned in order to fit to k that is the number of OR term within CLB. The partitioned nodes are merged through collapsing and bin packing is performed in order to fit to the number of OR term within CLB.

• The Transactions of the Korea Information Processing Society
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• v.6 no.12
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• pp.3523-3529
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• 1999

In this paper, we propose an improved regression testing method, which use method as the basic unit of changing. The testing method consists of three steps. We represent the relationship of classes using the notation of UML(Unified Modeling Language), find the nodes of the modified methods and affected methods by node analysis, and then select changed test cases from the original test cases. The proposed object-oriented regression testing method can reduce the number of test cases, testing time and cost through reuse of test cases.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.46 no.5
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• pp.1-7
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• 2009

When constructing various mobile networks including sensor networks, the problem of finding the layout or graph to interconnect the terminals or nodes of the network may come up. Providing a common scheme that can be applied to the kind of problems, and formulating the bounds of the run time and the result of the algorithm from the scheme, one may evaluate precisely the plan of constructing analogous network systems. This paper, dealing with EMST(Euclidean Minimum Spanning Tree) that represents such problems, provides the scheme for constructing EMST by parallel processing over distributed environments, and the ground for determining the maximum difference of the layout or the graph produced from the scheme: the difference from EMST. In addition, it provides the upper bound of the run time of the algorithm from the scheme.

• 한국정보교육학회:학술대회논문집
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• 2005.08a
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• pp.107-115
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• 2005

통계 학습에서 자바 애플릿의 활용은 통계간을 직관적으로 이해할 수 있어 학습의 효과를 높일 수 있다. 통계 영역의 그래프를 공부하는데 통계값의 의미를 눈으로 보면서 이해하고 직접 조작하며 학습을 하면 보다 능률적일 것이다. 이에 본 연구에서는 통계 영역의 교육과정을 분석하여 그래프 객체를 정의하고, 객체의 속성과 메소드를 분석하여 클래스를 구성한 후 클래스 사이의 관계를 파악하여 클래스 계층구조를 설계하였다.