• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.713-716
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• 2005

In this paper, in order to identify and recognize attack patterns, we propose a Bayesian classification using frequent patterns. In theory, Bayesian classifiers guarantee the minimum error rate compared to all other classifiers. However, in practice this is not always the case owing to inaccuracies in the unrealistic assumption{ class conditional independence) made for its use. Our method addresses the problem of attribute dependence by discovering frequent patterns. It generates frequent patterns using an efficient FP-growth approach. Since the volume of patterns produced can be large, we propose a pruning technique for selection only interesting patterns. Also, this method estimates the probability of a new case using different product approximations, where each product approximation assumes different independence of the attributes. Our experiments show that the proposed classifier achieves higher accuracy and is more efficient than other classifiers.

• Genomics & Informatics
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• v.10 no.4
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• pp.271-274
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• 2012

Whereas a vast amount of new information on bioinformatics is made available to the public through patents, only a small set of patents are cited in academic papers. A detailed analysis of registered bioinformatics patents, using the existing patent search system, can provide valuable information links between science and technology. However, it is extremely difficult to select keywords to capture bioinformatics patents, reflecting the convergence of several underlying technologies. No single word or even several words are sufficient to identify such patents. The analysis of patent subclasses can provide valuable information. In this paper, I did a preliminary study of the current status of bioinformatics patents and their International Patent Classification (IPC) groups registered in the Korea Intellectual Property Rights Information Service (KIPRIS) database.

• Journal of the Korea institute for structural maintenance and inspection
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• v.4 no.4
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• pp.129-140
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• 2000

Retaining wall is a constructed structure in order to construct road, rail, building for effective use and obtainments of the limited ground. Recently, many kinds of research have been actively developed for a standardization and information to the maintenance and management of bridge, tunnel, road. With the works of database construction of that, many kinds of data with respect to statistics is cumulated. Database work of retaining wall is wholly lacking and lagged behind in the works of database construction. This paper suggests classification system on inspection data. On the basis of that, code work with classification system was practised and DB program of inspection data of retaining wall was developed. And input work for a data of maintenance and management was practised. The purpose of this paper is to suggest a kind of statistics data and investigate a characteristics of inspection using statistic data on retaining wall.

• Asia pacific journal of information systems
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• v.5 no.2
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• pp.143-176
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• 1995

We present an overview of the Extensional Object Model (ExOM) and describe in detail the learning and classification components which integrate concepts from machine learning and object-oriented databases. The ExOM emphasizes flexibility in information acquisition, learning, and classification which are useful to support tasks such as diagnosis, planning, design, and database mining. As a vehicle to integrate machine learning and databases, the ExOM supports a broad range of learning and classification methods and integrates the learning and classification components with traditional database functions. To ensure the integrity of ExOM databases, a subsumption testing rule is developed that encompasses categories defined by type expressions as well as concept definitions generated by machine learning algorithms. A prototype of the learning and classification components of the ExOM is implemented in Smalltalk/V Windows.

• International Journal of Knowledge Content Development & Technology
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• v.2 no.1
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• pp.51-65
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• 2012

While science and technology information service portals and heterogeneous databases produced in Korea and other countries are integrated, methods of connecting the unique classification systems applied to each database have been studied. Results of technologists' research, such as, journal articles, patent specifications, and research reports, are organically related to each other. In this case, if the most basic and meaningful classification systems are not connected, it is difficult to achieve interoperability of the information and thus not easy to implement meaningful science technology information services through information convergence. This study aims to address the aforementioned issue by analyzing mapping systems between classification systems in order to design a structure to connect a variety of classification systems used in the academic information database of the Korea Institute of Science and Technology Information, which provides science and technology information portal service. This study also aims to design a mapping system for the classification systems to be applied to actual science and technology information services and information management systems.

• Journal of the Korean association of regional geographers
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• v.16 no.4
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• pp.428-439
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• 2010

Map of geographical names can give us information for understanding of region because geographical name reflects regional perception of human. This study aimed to make an web-based map by constructing database of geographical names. Main contents carried out research on methods for classification of geographical names, database construction, and mapping on the website. Geographical name classified into four categories of the physical geography, culture and historical geography, economic geography, and the other and also, 18 sub-categories by classification criteria. Geographical name designed to input by collecting geographical names from paper-based maps and vernacular place names only known to the local region. Fields of database consisted of address, coordinates, geographical name(hangeul, hanja), classification, explanation, photographs. Map of geographical names can be represented with regional geographical information. The result of research is expected to offer information for distribution of geographical names as well as regional interpretation.

• Proceedings of the Korea Contents Association Conference
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• 2011.05a
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• pp.293-294
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• 2011

KISTI collects the scientific and technical articles published in Korea and builds the Korean STI database for scientists. The number of papers exceeds one million. To improve the search efficiency of the database additional processing is required. Abstracting, classification, indexing and extracting is a traditional processing method adding value to information. Indexing and classification are useful tool to assist efficient retrieval. In this paper, authors propose a method to improve information retrieval efficiency by assigning classification code and index terms to records of Korean STI database.

• Proceedings of the Korea Information Processing Society Conference
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• 2008.11a
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• pp.325-327
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• 2008

In electrical industry, classification methodology has been an important issue for analyzing power consumption patterns. It has many applications including decisions on energy purchasing, load switching as well as helping in infrastructure development. Our aim in this work is to classify the electrical section and find potentially non-safe electrical sections. For this purpose, we use Emerging Patterns based classification. The classification method uses the aggregate score of emerging patterns to build classifier. The proposed methodology was applied to a set of electrical section data of the Korea power. The test data and relational electricity information and knowledge are supported by Korea Electric Power Research Institute (KEPRI).

• IEIE Transactions on Smart Processing and Computing
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• v.5 no.2
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• pp.94-99
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• 2016

Application layer attacks have for years posed an ever-serious threat to network security, since they always come after a technically legitimate connection has been established. In recent years, cyber criminals have turned to fully exploiting the web as a medium of communication to launch a variety of forbidden or illicit activities by spreading malicious automated software (auto-ware) such as adware, spyware, or bots. When this malicious auto-ware infects a network, it will act like a robot, mimic normal behavior of web access, and bypass the network firewall or intrusion detection system. Besides that, in a private and large network, with huge Hypertext Transfer Protocol (HTTP) traffic generated each day, communication behavior identification and classification of auto-ware is a challenge. In this paper, based on a previous study, analysis of auto-ware communication behavior, and with the addition of new features, a method for classification of HTTP auto-ware communication is proposed. For that, a Not Only Structured Query Language (NoSQL) database is applied to handle large volumes of unstructured HTTP requests captured every day. The method is tested with real HTTP traffic data collected through a proxy server of a private network, providing good results in the classification and detection of suspicious auto-ware web access.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.2C
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• pp.181-185
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• 2009

When we classify underwater transient signals with frame-by-frame decision, a database design method for reference feature vectors influences on the system performance such as size of database, computational burden and recognition rate. In this paper the LBG vector quantization algorithm is applied to reduction of the number of feature vectors for each reference signal for efficient classification of underwater transient signals. Experimental results have shown that drastic reduction of the database size can be achieved while maintaining the classification performance by using the LBG vector quantization.