• 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.

• Journal of Korean Institute of Industrial Engineers
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• v.29 no.1
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• pp.32-40
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• 2003

In this study, we develop the Genetic Fuzzy System(GFS) to estimate the link traveling speed. Based on the genetic algorithm, we can get the fuzzy rules and membership functions that reflect more accurate correlation between traffic data and speed. From the fact that there exist missing links that lack traffic data, we added a Case Base Reasoning(CBR) to GFS to support estimating the speed of missing links. The case base stores the fuzzy rules and membership functions as its instances. As cases are accumulated, the case base comes to offer appropriate cases to missing links. Experiments show that the proposed GFS provides the more accurate estimation of link traveling speed than existing methods.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2002.05a
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• pp.81-86
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• 2002

In current CBR(Case-Based Reasoning) systems, the case adaptation is usually performed by rule-based method that use rules hand-coded by the system developer. So, CBR system designer faces knowledge acquisition bottleneck similar to those found in traditional expert system design. In this thesis, 1 present a model for learning method of case adaptation knowledge using case base. The feature difference of each pair of cases are noted and become the antecedent part of an adaptation rule, the differences between the solutions in the compared cases become the consequent part of the rule. However, the number of rules that can possibly be discovered using a learning algorithm is enormous. The first method for finding cases to compare uses a syntactic measure of the distance between cases. The threshold fur identification of candidates for comparison is fixed th the maximum number of differences between the target and retrived case from all retrievals. The second method is to use similarity metric since the threshold method may not be an accurate measure. I suggest the elimination method of duplicate rules. In the elimination process, a confidence value is assigned to each rule based on its frequency. The learned adaptation rules is applied in riven target Problem. The basic. process involves search for all rules that handle at least one difference followed by a combination process in which complete solutions are built.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.22 no.51
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• pp.129-138
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• 1999

The aim of this research is the development of an automated fixture planning system for prismatic parts using the case-based reasoning (CBR). CBR is the problem solving paradigm that uses the similarity between a new problem and old cases to solve the new problem. This research uses CBR for the fixture planning. A case is composed with the information of the part, the components of fixture and the method of fixing for the part. The basic procedure is the retrieval and adaptation for the case, and this research presents the method of retrieval that selects most similar case to the new situation. The retrieval-step is divided into an index matching and an aggregated matching. The adaptation is accomplished by the modification, which transforms the selected case to the solution of the situation of the input part by the specified CBR algorithm. The components of fixture and the method of fixing are determined for a new part by the procedure.

• Journal of Integrative Natural Science
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• v.10 no.3
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• pp.162-170
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• 2017

In this paper, it is aim to implement a wheelchair monitoring system that provides users with customized medical services easily in everyday life, together with mobility guarantee, which is the most basic requirement of the elderly and disabled persons with physical disabilities. The case-based intelligent wheelchair monitoring system proposed in this study is based on a case-based k-NN algorithm, which implements a system for constructing and inferring examples of various biometric and environmental information of wheelchair users as a knowledge database and a monitoring interface for wheelchair users. In order to confirm the usefulness of the case-based k-NN algorithm, the SVM algorithm showed an average accuracy of 84.2% and the average accuracy of the proposed case-based k-NN algorithm was 86.2% And showed higher performance in terms of accuracy. The system implemented in this paper has the advantage of measuring biometric information and data communication regardless of time and place and it can provide customized service of wheelchair user through user friendly interface.

• Journal of Digital Contents Society
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• v.12 no.2
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• pp.217-223
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• 2011

AI(Artificial Intelligence) refers to the area of computer engineering and IT technology that focuses on the methodology and creation of intelligent agents. The Othello game is often produced with AI, since it is played with relatively simple rules on a board and on a limited space of 8 rows and 8 columns. Previous algorithms take longer time than desirable and often fail to face new circumstances, as they search for all the possible cases and rules. In order to solve this crucial weakness, we propose that a CBR algorithm be applied to Orthello. Case-Based Reasoning(CBR), is the process of solving new problems based on the solutions of the past similar problems. We can apply this process to Othello and expedite the process of computer reasoning for a solution to new cases based on the data from accumulated past cases. Then, these new solutions are dynamically added to the set of past cases so that it becomes harder for players(users) to be able to read the pattern. The proposed system in which a CBR algorithm is applied to the Othello game makes the computation process faster and the game harder to play.

• The KIPS Transactions:PartB
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• v.15B no.1
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• pp.45-52
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• 2008

As the recent development of the IT services, there is a urgent need of effective diagnosis system to present appropriate solution for the complicated problems of breakdown control, a cause analysis of breakdown and others. So we propose an intelligent diagnosis system that integrates the case-based reasoning and the artificial neural network to improve the system performance and to achieve optimal diagnosis. The case-based reasoning is a reasoning method that resolves the problems presented in current time through the past cases (experience). And it enables to make efficient reasoning by means of less complicated knowledge acquisition process, especially in the domain where it is difficult to extract formal rules. However, reasoning by using the case-based reasoning alone in diagnosis problem domain causes a problem of suggesting multiple causes on a given symptom. Since the suggested multiple causes of given symptom has the same weight, the unnecessary causes are also examined as well. In order to resolve such problems, the back-propagation learning algorithm of the artificial neural network is used to train the pairs of the causes and associated symptoms and find out the cause with the highest weight for occurrence to make more clarified and reliable diagnosis.

• Korean Journal of Construction Engineering and Management
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• v.21 no.1
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• pp.50-60
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• 2020

Estimates of project costs in the early stages of a construction project have a significant impact on the operator's decision-making in important matters, such as the site's decision or the construction period. However, it is difficult to carry out the initial stage with confidence because information such as design books and specifications is not available. In previous studies, case-based reasoning was used to predict initial construction costs, and genetic algorithms were used to calculate the weight of the inquiry phase among them. However, some say that it is difficult to perform better than the current year because existing genetic algorithms are calculated in random numbers. To overcome these limitations, correlation numbers using correlation analysis rather than random numbers are reflected in the genetic algorithm by method of local search, and weights are calculated using a hybrid genetic algorithm that combines local search and genetic algorithms. A case-based reasoning model was developed using the weights calculated and validated with the data. As a result, it was found that the hybrid GA-CBR applied with local search performed better than the existing GA-CBR.

• Asia pacific journal of information systems
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• v.12 no.2
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• pp.179-195
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• 2002

In this research, we developed an efficient Hangul document classification system for text mining. We mean 'efficient' by maintaining an acceptable classification performance while taking shorter computing time. In our system, given a query document, k documents are first retrieved from the document case base using the k-nearest neighbor technique, which is the main algorithm of case-based reasoning. Then, TFIDF method, which is the traditional vector model in information retrieval technique, is applied to the query document and the k retrieved documents to classify the query document. We call this procedure 'CB_TFIDF' method. The result of our research showed that the classification accuracy of CB_TFIDF was similar to that of traditional TFIDF method. However, the average time for classifying one document decreased remarkably.

• Journal of Physiology & Pathology in Korean Medicine
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• v.23 no.5
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• pp.942-949
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• 2009

We in this paper propose the method for diagnosis patients through the reasoning based on the diagnosis ontology in oriental medicine. In prior studies, it is simply diagnosed with the information of main symptoms, optional symptoms, and tongue / pulse. In addition, ontology itself has subjective opinions of oriental medical doctors for patients in form of axioms. There is a problem in latter case that it is difficult for other oriental medical doctors to change knowledge within the ontology. In order to solve these problems, we have constructed the diagnosis ontology and the reasoning algorithm as followings: First, in order to raise the diagnosis accuracy, we constructed the diagnosis ontology with pattern identifications, main symptoms, optional symptoms, and tongue / pulse. We also utilize the diagnosis points described in the pathology textbook, which has been studied in all of domestic oriental medical colleges. This information is represented as OWL instances in ontology, not OWL axioms so that it can be easily updated. Second, we suggest the algorithms for diagnosis reasoning and learning method based on the ontology. We have implemented the reasoning and learning system according to the diagnosis algorithm. In future study, we will construct the diagnosis ontology with all of pattern identifications and symptoms within the pathology textbook.