• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.26 no.12
• /
• pp.1872-1879
• /
• 2022

Recently, with the development of artificial intelligence technology, research to use artificial intelligence to detect hacking attacks is being actively conducted. However, the fact that security data is a representative imbalanced data is recognized as a major obstacle in composing the learning data, which is the key to the development of artificial intelligence models. Therefore, in this paper, we propose a W-VAE oversampling technique that applies VAE, a deep learning generation model, to data extraction for oversampling, and sets the number of oversampling for each class through weight calculation using K-NN for sampling. In this paper, a total of five oversampling techniques such as ROS, SMOTE, and ADASYN were applied through NSL-KDD, an open network security dataset. The oversampling method proposed in this paper proved to be the most effective sampling method compared to the existing oversampling method through the F1-Score evaluation index.

• ETRI Journal
• /
• v.34 no.6
• /
• pp.847-857
• /
• 2012

Intrusion detection systems (IDSs) have an important effect on system defense and security. Recently, most IDS methods have used transformed features, selected features, or original features. Both feature transformation and feature selection have their advantages. Neighborhood component analysis feature transformation and genetic feature selection (NCAGAFS) is proposed in this research. NCAGAFS is based on soft computing and data mining and uses the advantages of both transformation and selection. This method transforms features via neighborhood component analysis and chooses the best features with a classifier based on a genetic feature selection method. This novel approach is verified using the KDD Cup99 dataset, demonstrating higher performances than other well-known methods under various classifiers have demonstrated.

• Korea Information Processing Society Review
• /
• v.11 no.2
• /
• pp.104-109
• /
• 2004

KT는 음성언어기술 연구를 10여년 이상 진행하여 왔다. 1990년 초에는 음성번역 연구를 중심으로 기초기술을 수행하여 왔으며 그 결과 1995년 5월 16일에 한-일 간 호텔예약을 위한 자동통역시험을 전자통신연구원, 일본 KDD(국제전신전화회사)와 성공적으로 수행하였다. 1995년 이후부터 유/무선통신망 응용연구 및 개발을 중심으로 수행하였다. 먼저 음성인식 증권정보 서비스를 1995년 가을부터 국내 최초로 시험서비스를 수행하였으며 1998년에는 음성인식 무인자동 교환서비스도 개발하였다(1). 2000년부터는 VoiceXML기 반 음성 언어 시스템을 개발하였으며 이를 지능망(Advanced Intelligent Network)에 수용하기 위해서 IP(Intelligent Peripheral) 에 음성인식, 음성합성기 및 VoiceXML 해석기를 통합하기 위한 연으로 다양한 음성언어서비스를 개발하여 출시하기 시작하였다［2］［3］［4］.(중략)

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2003.09a
• /
• pp.224-227
• /
• 2003

The Intrsuion Detecion Systems(IDS) are required the accuracy, the adaptability, and the expansion in the information society to be changed quickly. Also, it is required the more structured, and intelligent IDS to protect the resource which is important and maintains a secret in the complicated network environment. The research has the purpose to build the model for the intelligent IDS, which creates the intrusion patterns. The intrusion pattern has extracted from the vast amount of data. To manage the large size of data accurately and efficiently, the link analysis and sequence analysis among the data mining techniqes are used to build the model creating the intrusion patterns. The model is consist of "Time based Traffic Model", "Host based Traffic Model", and "Content Model", which is produced the different intrusion patterns with each model. The model can be created the stable patterns efficiently. That is, we can build the intrusion detection model based on the intelligent systems. The rules prodeuced by the model become the rule to be represented the intrusion data, and classify the normal and abnormal users. The data to be used are KDD audit data.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2003.09a
• /
• pp.660-663
• /
• 2003

The advanced computer network technology enables connectivity of computers through an open network environment. There has been growing numbers of security threat to the networks. Therefore, it requires intrusion detection and prevention technologies. In this paper, we propose a network based intrusion detection model using Fuzzy Cognitive Maps(FCM) that can detect intrusion by the Denial of Service(DoS) attack detection method adopting the packet analyses. A DoS attack appears in the form of the Probe and Syn Flooding attack which is a typical example. The Sp flooding Preventer using Fuzzy cognitive maps(SPuF) model captures and analyzes the packet information to detect Syn flooding attack. Using the result of analysis of decision module, which utilized FCM, the decision module measures the degree of danger of the DoS and trains the response module to deal with attacks. The result of simulating the "KDD ′99 Competition Data Set" in the SPuF model shows that the Probe detection rates were over 97 percentages.

• Journal of Korea Society of Industrial Information Systems
• /
• v.2 no.2
• /
• pp.189-207
• /
• 1997

Recently, we have witnessed a host of emerging tools in the management support systems (MSS) area including the data warehouse/multidimensinal databases (MDDB), data mining, on-line analytical processing (OLAP), intelligent agents, World Wide Web(WWW) technologies, the Internet, and corporate intranets. These tools are reshaping MSS developments in organizations. This article reviews a set of emerging data management technologies in the knowledge discovery in databases(KDD) process and analyzes their implications for decision support. Furthermore, today's MSS are equipped with a plethora of AI techniques (artifical neural networks, and genetic algorithms, etc) fuzzy sets, modeling by example , geographical information system(GIS), logic modeling, and visual interactive modeling (VIM) , All these developments suggest that we are shifting the corporate decision making paradigm form information-driven decision making in the1980s to knowledge-driven decision making in the 1990s.

• Proceedings of the Korea Institutes of Information Security and Cryptology Conference
• /
• 2006.06a
• /
• pp.649-653
• /
• 2006

In this paper, we propose a hybrid feature extraction method in which Principal Components Analysis is combined with optimized k-Means clustering technique. Our approach hierarchically reduces the redundancy of features with high explanation in principal components analysis for choosing a good subset of features critical to improve the performance of classifiers. Based on this result, we evaluate the performance of intrusion detection by using Support Vector Machine and a nonparametric approach based on k-Nearest Neighbor over data sets with reduced features. The Experiment results with KDD Cup 1999 dataset show several advantages in terms of computational complexity and our method achieves significant detection rate which shows possibility of detecting successfully attacks.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.23 no.57
• /
• pp.11-20
• /
• 2000

In dynamic management environment, CEO must make an efficient decision with information & knowledge management systems based on IT(Information Technology). As a key component to cope with this current, we suggest the business performance analysis system based on KDD(Knowledge Discovery in Databases). We consider the theoretical model that is composited both Value-Added in respect of stakeholder and Economic Value-Added in respect of shareholder. Additionally we use DBMS and data mining method using Genetic Algorithms as physical model. To demonstrate the performance of the business performance analysis system, we analyse a domestic motors industry. The empirical case is based on the financial data of KISFAS(Korea Investors Services Financial Analysis System) database. The samples included in the study consist of H motors/S motors industry over the 16-year from 1981 to 1996.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.11 no.7
• /
• pp.633-640
• /
• 2001

In this paper we examine the machine learning issues raised by the domain of the Intrusion Detection Systems(IDS), which have difficulty successfully classifying intruders. There systems also require a significant amount of computational overhead making it difficult to create robust real-time IDS. Machine learning techniques can reduce the human effort required to build these systems and can improve their performance. Genetic algorithms are used to improve the performance of search problems, while data mining has been used for data analysis. Data Mining is the exploration and analysis of large quantities of data to discover meaningful patterns and rules. Among the tasks for data mining, we concentrate the classification task. Since classification is the basic element of human way of thinking, it is a well-studied problem in a wide variety of application. In this paper, we propose a classifier system based on genetic algorithm, and the proposed system is evaluated by applying it to IDS problem related to classification task in data mining. We report our experiments in using these method on KDD audit data.

• International journal of advanced smart convergence
• /
• v.6 no.3
• /
• pp.22-28
• /
• 2017

With the recent development of technologies to manage vast amounts of data, data mining technology has had a major impact on all industries.. Data mining is the process of discovering useful correlations hidden in data, extracting executable information for the future, and using it for decision making. In other words, it is a core process of Knowledge Discovery in data base(KDD) that transforms input data and derives useful information. It extracts information that we did not know until now from a large data base. In the decision tree, c4.5 algorithm was used. In addition, the C4.5 algorithm was used in the decision tree to analyze the difference between frequency and mortality in the region. In this paper, the frequency and mortality of percutaneous coronary intervention for patients with heart disease were divided into regions.