• Journal of the Korea Institute of Information Security & Cryptology
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• v.25 no.1
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• pp.107-122
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• 2015

Many defensive mechanisms have been evolved as new attack methods are developed. However, APT attacks using e-mail are still hard to detect and prevent. Recently, many organizations in the government sector or private sector have been hacked by malicious e-mail based APT attacks. In this paper, first, we built hacking e-mail database based on the real e-mail data which were used in attacks on the Korean government organizations in recent years. Then, we extracted features from the hacking e-mails for profiling them. We design a case vector that can describe the specific characteristics of hacking e-mails well. Finally, based on case based reasoning, we made an algorithm for retrieving the most similar case from the hacking e-mail database when a new hacking e-mail is found. As a result, hacking e-mails have common characteristics in several features such as geo-location information, and these features can be used for classifying benign e-mails and malicious e-mails. Furthermore, this proposed case based reasoning algorithm can be useful for making a decision to analyze suspicious e-mails.

• Proceedings of the KSME Conference
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• 2001.06c
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• pp.240-245
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• 2001

In axiomatic approach for design evaluation, even if a mutual relation don't appear physical mapping of high level, it can appear in process mapping of low level through coupled PVs(Process Variables), but we must solve it for correct design evaluation. This paper handle a method for solving of coupled PVs by using axiomatic approach and CBR(Case-Based Reasoning). The methodology of proposal took still more shape through the instance of MCPs(Microcellular Plastics).

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2005.11a
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• pp.571-578
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• 2005

Corporate credit rating analysis has drawn a lot of research interests in previous studies, and recent studies have shown that machine learning techniques achieved better performance than traditional statistical ones. This paper applies support vector machines (SVMs) to the corporate credit rating problem in an attempt to suggest a new model with better explanatory power and stability. To serve this purpose, the researcher uses a grid-search technique using 5-fold cross-validation to find out the optimal parameter values of kernel function of SVM. In addition, to evaluate the prediction accuracy of SVM, the researcher compares its performance with those of multiple discriminant analysis (MDA), case-based reasoning (CBR), and three-layer fully connected back-propagation neural networks (BPNs). The experiment results show that SVM outperforms the other methods.

• Journal of Intelligence and Information Systems
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• v.7 no.1
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• pp.47-61
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• 2001

Lazy loaming methods including CBR have relative advantages in comparison with eager loaming methods such as artificial neural networks and decision trees. However, they are very sensitive to irrelevant features. In other words, when there are irrelevant features, larry learning methods have difficulty in comparing cases. Therefore, their performance can be degraded significantly. To overcome this disadvantage, feature weighting methods for lazy loaming methods have been studied. Most of the existing researches, however, were focused on global feature weighting. In this research, we propose a new local feature weighting method, which we shall call CBDFW. CBDFW stores classification performance of randomly generated feature weight vectors. Then, given a new query case, CBDFW retrieves the successful feature weight vectors and designs a feature weight vector fur the query case. In the test on credit evaluation domain, CBDFW showed better classification accuracy when compared to the results of previous researches.

• Transactions of the Korean Society of Automotive Engineers
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• v.13 no.5
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• pp.163-170
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• 2005

Urban transit is a complex system that is combined electrically and mechanically, it is necessary to construct maintenance system for securing safety accompanying high-speed driving and maintaining promptly. Expert system is a computer program which uses numerical or non-numerical domain-specific knowledge to solve problems. In this research, we intend to develop the expert system which diagnose failure causes quickly and display measures. For the development of expert system, standardization of failure code classification system and creation of BOM(Bill Of Materials) have been first performed. Through the analysis of failure history and maintenance manuals, knowledge base has been constructed. Also, for retrieving the procedure of failure diagnosis and repair linking with the knowledge base, we have built RBR(Rule Based Reasoning) engine by pattern matching technique and CBR(Case Based Reasoning) engine by similarity search method. This system has been developed based on web to maximize the accessibility.

• Journal of Information Technology Services
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• v.17 no.1
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• pp.91-103
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• 2018

This study developed a prediction model using machine learning technology and predicted the success of health consulting by using life log data generated through u-Health service. The model index of the Random Forest model was the highest using. As a result of analyzing the Random Forest model, blood pressure was the most influential factor in the success or failure of metabolic syndrome in the subjects of u-Health service, followed by triglycerides, body weight, blood sugar, high cholesterol, and medication appear. muscular, basal metabolic rate and high-density lipoprotein cholesterol were increased; waist circumference, Blood sugar and triglyceride were decreased. Further, biometrics and health behavior improved. After nine months of u-health services, the number of subjects with four or more factors for metabolic syndrome decreased by 28.6%; 3.7% of regular drinkers stopped drinking; 23.2% of subjects who rarely exercised began to exercise twice a week or more; and 20.0% of smokers stopped smoking. If the predictive model developed in this study is linked with CBR, it can be used as case study data of CBR with high probability of success in the prediction model to improve the compliance of the subject and to improve the qualitative effect of counseling for the improvement of the metabolic syndrome.

• Journal of the Korean Operations Research and Management Science Society
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• v.23 no.1
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• pp.109-141
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• 1998

Many processes in the industrial realm exhibit sstochastic and nonlinear behavior. Consequently, an intelligent system must be able to nonlinear production processes as well as probabilistic phenomena. In order for a knowledge based system to control a manufacturing processes as well as probabilistic phenomena. In order for a knowledge based system to control manufacturing process, an important capability is that of prediction : forecasting the future trajectory of a process as well as the consequences of the control action. This paper examines the robustness of data mining tools under varying levels of noise while predicting nonlinear processes, includinb chaotic behavior. The evaluated models include the perceptron neural network using backpropagation (BPN), the recurrent neural network (RNN) and case based reasoning (CBR). The concepts are crystallized through a case study in predicting a chaotic process in the presence of various patterns of noise.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.19 no.40
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• pp.81-90
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• 1996

It is one of the most important thing to select pneumatic components in pneumatic system design. For the purpose of selecting pneumatic components, case objects are described as a knowledge representation and the most similar case object must be selected by decision making in computer. In this paper, case objects are represented using the methodology that is used for CBR(Case Base Reasoning) and methodology that the most similar case can be selected is Proposed. Algorithm VIWNNR(Varying Index Weight-based Nearer Neighbor Retrieval) is accomplished by varying index weight, that is not considering a index matching as true or false but varying a size of weight according to the degree of matching and enhance the flexibility of SCRM(Similar Case Retrieval Module) involving fuzzy concept in matching the cases. SCRM is tested In verify the feasibility to select pneumatic linear components and is peformed effectively.

• Proceedings of the KAIS Fall Conference
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• 2003.11a
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• pp.185-190
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• 2003

Due to the rapid growth of Internet, means of communication with customers in a traditional customer support environment such as telephone calls are being replaced by mainly e-mail in a Web-based customer support system. Although such a Web-based support is efficient and promises potential benefits for firms, including reduced transaction costs, reduced time, and high quality of support, there are some difficulties associated with responding to many types of customer’s inbound e-mails appropriately .As many types of e-mail are received, considerable attention is being paid to methods for increasing the efficiency of managing and responding e-mails. This research proposes an intelligent system for managing customer’s inbound e-mails in organizations by applying case based reasoning technique for responding to various customers' inbound e-mails more effectively. In this approach, a case is represented as a frame-typed data structure corresponding to an inbound e-mail, keywords, and its reply e-mail. In the retrieval procedure, keywords and affinity set is developed to index a case, and then the case is represented as a vector, a case vector. Also, cosines value is calculated to measure the similarity between a new inbound e-mail and the cases in the case base. In the adaptation procedure, we provide several adaptation strategies to adapt and modify the retrieved case. The strategies guide to make an outbound e-mail using product databases, databases for customer support, etc. Additionally, the Web-based system architecture is proposed to implement our methodology. The proposed methodology and system will be helpful for developing more efficient Web-based customer support.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.2 no.1
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• pp.13-17
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• 2001

The configuration design of kinematic mechanisms of industrial sewing machine has been studied using a functional approach. The configuration design methodology has been applied to shorten the development cycle time of mechanisms and to manage design data efficiently Expert system has been used to embody the decomposition of functional requirements. It has been interfaced with a CAD system through the API program to show the assembly and parts of the mechanism. Constraints also can be handled by the expert system through the rule induction and the case based reasoning process. The configuration design system includes the kinematical analysis and optimization of the mechanisms of an industrial sewing machine by the interface between the expert system and an analysis program by means of API Program supplied by expert system. The conceptual design of sewing machine mechanism can be Performed rapidly and efficiently.