• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2004년도 ICCAS
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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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• 제22권12호
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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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• 제5권4호
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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.

• 한국콘텐츠학회논문지
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• 제8권1호
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• pp.68-74
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• 2008

데이터베이스로부터 지식을 발견하고 이를 연구기획자, 정책의사결정자들이 활용하는 움직임이 전세계적으로 활발해지고 있다. 이러한 연구분야 중 대표적인 것이 계량정보학이고 이 분야를 지원하기 위해서 주로 선진국을 중심으로 분석시스템이 개발되고 있다. 그러나 외국의 분석시스템은 실제 수요자의 요구를 충분히 반영하지 못하고 있고, 고가이면서 한글이 지원되지 않아 국내 연구기획자가 사용하기에 어려운 점이 있다. 따라서 한국과학기술정보연구원에서는 이러한 단점을 극복하기 위해서 계량정보분석시스템 KnowledgeMatrix를 개발하였다. KnowledgeMatrix는 논문 및 특허의 서지정보를 분석하여 지식을 발견하기 위한 목적으로 설계된 독립형(stand-alone) 시스템이다 KnowledgeMatrix의 주요 구성을 살펴보면 행렬 생성, 클러스터링, 시각화, 데이터 전처리로 요약된다. 본 논문에서 소개하고 있는 KnowledgeMatrix는 외국의 대표적인 정보분석시스템과 비교했을 때 다양한 기능을 제공하고 있고 특히 영문데이터 처리 이외에 한글데이터 처리가 가능하다는 장점을 갖고 있다.

• 대한기계학회논문집 C: 기술과 교육
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• 제4권1호
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• pp.27-34
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• 2016

지열 시스템을 대상으로 데이터 마이닝 기반 성능 예측 모델을 구축하였다. 지열 시스템의 실시간 성능 분석과 예측에 필요한 데이터의 기본 조건을 검토한 후, 데이터베이스의 구조를 설계하였다. 먼저 시스템 성능계수(COP)와 전력 소비량을 분석 대상으로 설정한 후, 이들 물리량의 추출 주기(1분 5분 10분 30분 60분 간격)가 예측 결과에 미치는 영향을 분석하였다. 이어서 범주형과 수치형 의사결정나무 모델을 적용하여 시스템의 성능을 예측하였다. 범주형 의사결정나무 모델을 적용했을 때, 10분 주기의 예측 결과의 정확도는 97.7%로 가장 높았다. 또한 수치형 의사결정나무 분석 결과를 통해 COP가 변하는 순간의 임계값을 찾을 수 있었다. 본 논문에서 제안한 방법은 지열 시스템의 실시간 성능 분석과 운전 상태 등에 적용할 수 있을 것으로 판단된다.