• 전기학회논문지
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• 제67권9호
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• pp.1146-1151
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• 2018

Recently, researches on the intelligence of electric power facilities have been trying to apply artificial intelligence techniques as computer platforms have improved. In particular, faults occurring in substation should be able to quickly identify possible faults and minimize power fault recovery time. This paper presents fault location technique for 154kV substation using neural network. We constructed a training matrix based on the operating conditions of the circuit breaker and IED to identify the fault location of each component of the target 154kV substation, such as line, bus, and transformer. After performing the training to identify the fault location by the neural network using Weka software, the performance of fault location discrimination of the designed neural network was confirmed.

• International Journal of Computer Science & Network Security
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• 제22권5호
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• pp.277-283
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• 2022

Email phishing has become very prevalent especially now that most of our dealings have become technical. The victim receives a message that looks as if it was sent from a known party and the attack is carried out through a fake cookie that includes a phishing program or through links connected to fake websites, in both cases the goal is to install malicious software on the user's device or direct him to a fake website. Today it is difficult to deploy robust cybersecurity solutions without relying heavily on machine learning algorithms. This research seeks to detect phishing emails using high-accuracy machine learning techniques. using the WEKA tool with data preprocessing we create a proposed methodology to detect emails phishing. outperformed random forest algorithm on Naïve Bayes algorithms by accuracy of 99.03 %.

• 디지털융복합연구
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• 제16권5호
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• pp.399-406
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• 2018

인공지능을 이용한 침입탐지 연구는 KDDCup99 데이터 세트를 사용하여 많은 연구가 이루어졌다. 이전 연구에서 SMO(SVM)알고리즘의 성능이 우수하다고 알려져 있다. 하지만 훈련에 사용되지 않은 새로운 침입유형의 침입탐지연구는 미비하다. 본 논문에서는 웨카(weka)의 SMO와 KDDCup99 훈련 데이터 세트인 kddcup.data.gz의 인스턴스를 이용하여 모델을 생성하였다. corrected.gz 파일의 인스턴스 중 기존 침입(292,300개)과 새로운 침입(18,729개)을 테스트하였다. 일반적으로 훈련에 사용되지 않은 침입 라벨은 테스트 되지 않기 때문에 새로운 침입라벨을 normal.로 변경하여 테스트하였다. 새로운 침입 18,729개의 인스턴스 중 1,827개는 침입으로 분류하였다. 새로운 침입으로 분류한 1,827개의 인스턴스는 buffer_overflow. 3개, neptune. 392개, portsweep. 164개, ipsweep. 9개, back. 511개, imap. 1개, satan. 개, 645 개, nmap. 102개로 분류되었다.

• 한국인공지능학회지
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• 제11권2호
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• pp.19-27
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• 2023

In recent times, an exponential increase in Internet traffic has been observed as a result of advancing development of the Internet of Things, mobile networks with sensors, and communication functions within various devices. Further, the COVID-19 pandemic has inevitably led to an explosion of social network traffic. Within this context, considerable attention has been drawn to research on network traffic analysis based on machine learning. In this paper, we design and develop a new machine learning framework for network traffic analysis whereby normal and abnormal traffic is distinguished from one another. To achieve this, we combine together well-known machine learning algorithms and network traffic analysis techniques. Using one of the most widely used datasets KDD CUP'99 in the Weka and Apache Spark environments, we compare and investigate results obtained from time series type analysis of various aspects including malicious codes, feature extraction, data formalization, network traffic measurement tool implementation. Experimental analysis showed that while both the logistic regression and the support vector machine algorithm were excellent for performance evaluation, among these, the logistic regression algorithm performs better. The quantitative analysis results of our proposed machine learning framework show that this approach is reliable and practical, and the performance of the proposed system and another paper is compared and analyzed. In addition, we determined that the framework developed in the Apache Spark environment exhibits a much faster processing speed in the Spark environment than in Weka as there are more datasets used to create and classify machine learning models.

• 전기학회논문지
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• 제67권4호
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• pp.569-577
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• 2018

In this paper, with the aid of information which is included within data, preprocessing algorithm-based black plastic classifier is designed. The slope and area of spectrum obtained by using laser induced breakdown spectroscopy(LIBS) are analyzed for each material and its ensuing information is applied as the input data of the proposed classifier. The slope is represented by the rate of change of wavelength and intensity. Also, the area is calculated by the wavelength of the spectrum peak where the material property of chemical elements such as carbon and hydrogen appears. Using informations such as slope and area, input data of the proposed classifier is constructed. In the preprocessing part of the classifier, Principal Component Analysis(PCA) and fuzzy transform are used for dimensional reduction from high dimensional input variables to low dimensional input variables. Characteristic analysis of the materials as well as the processing speed of the classifier is improved. In the condition part, FCM clustering is applied and linear function is used as connection weight in the conclusion part. By means of Particle Swarm Optimization(PSO), parameters such as the number of clusters, fuzzification coefficient and the number of input variables are optimized. To demonstrate the superiority of classification performance, classification rate is compared by using WEKA 3.8 data mining software which contains various classifiers such as Naivebayes, SVM and Multilayer perceptron.

• 디지털산업정보학회논문지
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• 제11권4호
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• pp.33-45
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• 2015

Currently, Internet is used an essential tool in the business area. Despite this importance, there is a risk of network attacks attempting collection of fraudulence, private information, and cyber terrorism. Firewalls and IDS(Intrusion Detection System) are tools against those attacks. IDS is used to determine whether a network data is a network attack. IDS analyzes the network data using various techniques including expert system, data mining, and state transition analysis. This paper tries to compare the performance of two data mining models in detecting network attacks. They are decision tree (C4.5), and neural network (FANN model). I trained and tested these models with data and measured the effectiveness in terms of detection accuracy, detection rate, and false alarm rate. This paper tries to find out which model is effective in intrusion detection. In the analysis, I used KDD Cup 99 data which is a benchmark data in intrusion detection research. I used an open source Weka software for C4.5 model, and C++ code available for FANN model.

• International Journal of Advanced Culture Technology
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• 제10권4호
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• pp.569-575
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• 2022

This is a research thesis on the development of a monitoring and diagnosis device that prevents the risk of an accident through monitoring and diagnosis of a modular Main Circuit Breaker (MCB) using Vacuum Interrupter (VI) for Korean high-speed rolling stock. In this paper, a comprehensive MCB monitoring and diagnosis was performed by converging vacuum level diagnosis of interrupter, operating coil monitoring of MCB and environmental temperature/humidity monitoring of modular box. In addition, to develop an algorithm that is expected to have a similar data processing before the actual field test of the MCB monitoring and diagnosis device in 2023, the cluster analysis and factor analysis were performed using the WEKA data mining technique on the big data of Korean railroad transformer, which was previously researched by Tae Hee Evolution with KORAIL.

• 대한치과교정학회지
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• 제52권2호
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• pp.102-111
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• 2022

Objective: To develop and explore the usefulness of an artificial intelligence system for the prediction of the need for dental extractions during orthodontic treatments based on gender, model variables, and cephalometric records. Methods: The gender, model variables, and radiographic records of 214 patients were obtained from an anonymized data bank containing 314 cases treated by two experienced orthodontists. The data were processed using an automated machine learning software (Auto-WEKA) and used to predict the need for extractions. Results: By generating and comparing several prediction models, an accuracy of 93.9% was achieved for determining whether extraction is required or not based on the model and radiographic data. When only model variables were used, an accuracy of 87.4% was attained, whereas a 72.7% accuracy was achieved if only cephalometric information was used. Conclusions: The use of an automated machine learning system allows the generation of orthodontic extraction prediction models. The accuracy of the optimal extraction prediction models increases with the combination of model and cephalometric data for the analytical process.