• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.2029-2032
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• 2004

The quantity of data were rapidly increased recently and caused the data overwhelming. This led to be difficult in searching the required data. The method of eliminating redundant data was needed. One of the efficient methods was Knowledge Discovery in Database (KDD). Generally data can be separate into 2 cases, continuous data and discrete data. This paper describes algorithm that transforms continuous attributes into discrete ones. We present an Improved Class Attribute Interdependence Maximization (ICAIM), which designed to work with supervised data, for discretized process. The algorithm does not require user to predefine the number of intervals. ICAIM improved CAIM by using significant test to determine which interval should be merged to one interval. Our goal is to generate a minimal number of discrete intervals and improve accuracy for classified class. We used iris plant dataset (IRIS) to test this algorithm compare with CAIM algorithm.

• International Journal of Computer Science & Network Security
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• v.22 no.12
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• pp.185-196
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• 2022

The Internet of Things (IoT) has become more and more widespread in recent years, thus attackers are placing greater emphasis on IoT environments. The IoT connects a large number of smart devices via wired and wireless networks that incorporate sensors or actuators in order to produce and share meaningful information. Attackers employed IoT devices as bots to assault the target server; however, because of their resource limitations, these devices are easily infected with IoT malware. The Distributed Denial of Service (DDoS) is one of the many security problems that might arise in an IoT context. DDOS attempt involves flooding a target server with irrelevant requests in an effort to disrupt it fully or partially. This worst practice blocks the legitimate user requests from being processed. We explored an intelligent intrusion detection system (IIDS) using a particular sort of machine learning, such as Artificial Neural Networks, (ANN) in order to handle and mitigate this type of cyber-attacks. In this research paper Feed-Forward Neural Network (FNN) is tested for detecting the DDOS attacks using a modified version of the KDD Cup 99 dataset. The aim of this paper is to determine the performance of the most effective and efficient Back-propagation algorithms among several algorithms and check the potential capability of ANN- based network model as a classifier to counteract the cyber-attacks in IoT environments. We have found that except Gradient Descent with Momentum Algorithm, the success rate obtained by the other three optimized and effective Back- Propagation algorithms is above 99.00%. The experimental findings showed that the accuracy rate of the proposed method using ANN is satisfactory.

• Journal of Computing Science and Engineering
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• v.5 no.4
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• pp.305-313
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• 2011

Due to the expansion of high-speed Internet access, the need for secure and reliable networks has become more critical. The sophistication of network attacks, as well as their severity, has also increased recently. As such, more and more organizations are becoming vulnerable to attack. The aim of this research is to classify network attacks using neural networks (NN), which leads to a higher detection rate and a lower false alarm rate in a shorter time. This paper focuses on two classification types: a single class (normal, or attack), and a multi class (normal, DoS, PRB, R2L, U2R), where the category of attack is also detected by the NN. Extensive analysis is conducted in order to assess the translation of symbolic data, partitioning of the training data and the complexity of the architecture. This paper investigates two engines; the first engine is the back-propagation neural network intrusion detection system (BPNNIDS) and the second engine is the radial basis function neural network intrusion detection system (BPNNIDS). The two engines proposed in this paper are tested against traditional and other machine learning algorithms using a common dataset: the DARPA 98 KDD99 benchmark dataset from International Knowledge Discovery and Data Mining Tools. BPNNIDS shows a superior response compared to the other techniques reported in literature especially in terms of response time, detection rate and false positive rate.

• The Journal of the Korea Contents Association
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• v.8 no.1
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• pp.68-74
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• 2008

Application areas of Knowledge Discovery in Database(KDD) have been expanded to many R&D management processes including technology trends analysis, forecasting and evaluation etc. Established research field such as informetrics (or scientometrics) has utilized techniques or methods of KDD. Various systems have been developed to support works of analyzing large-scale R&D related databases such as patent DB or bibliographic DB by a few researchers or institutions. But extant systems have some problems for korean users to use. Their prices is not moderate, korean language processing is impossible, and user's demands not reflected. To solve these problems, Korea Institute of Science and Technology Information(KISTI) developed stand-alone type information analysis system named as KnowledgeMatrix. KnowledgeMatrix system offer various functions to analyze retrieved data set from databases. KnowledgeMatrix's main operation unit is composed of user-defined lists and matrix generation, cluster analysis, visualization, data pre-processing. Matrix generation unit help extract information items which will be analyzed, and calculate occurrence, co-occurrence, proximity of the items. Cluster analysis unit enable matrix data to be clustered by hierarchical or non-hierarchical clustering methods and present tree-type structure of clustered data. Visualization unit offer various methods such as chart, FDP, strategic diagram and PFNet. Data pre-processing unit consists of data import editor, string editor, thesaurus editor, grouping method, field-refining methods and sub-dataset generation methods. KnowledgeMatrix show better performances and offer more various functions than extant systems.

• Transactions of the KSME C: Technology and Education
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• v.4 no.1
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• pp.27-34
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• 2016

This preliminary study investigated data mining-based methods to assess and predict the performance of geothermal heat pump(GHP) system. Data mining is a key process of the knowledge discovery in database (KDD), which includes five steps: 1) Selection; 2) Pre-processing; 3) Transformation; 4) Analysis(data mining); and 5) Interpretation/Evaluation. We used two analysis models, categorical and numerical decision tree models to ascertain the patterns of performance(COP) and electrical consumption of the GHP system. Prior to applying the decision tree models, we statistically analyzed measurement database to determine the effect of sampling intervals on the system performance. Analysis results showed that 10-min sampling data for the performance analysis had highest accuracy of 97.7% over the actual dataset of the GHP system.