[KSCI] Korea Science Citation Index Service

http://dx.doi.org/10.13089/JKIISC.2003.13.2.99

Anomaly Detection Scheme Using Data Mining Methods

박광진 (광운대학교 컴퓨터과학과)
유황빈 (광운대학교 컴퓨터과학과)

Publication Information

Journal of the Korea Institute of Information Security & Cryptology / v.13, no.2, 2003 , pp. 99-106 More about this Journal

Abstract

Intrusions pose a serious security risk in a network environment. For detecting the intrusion effectively, many researches have developed data mining framework for constructing intrusion detection modules. Traditional anomaly detection techniques focus on detecting anomalies in new data after training on normal data. To detect anomalous behavior, Precise normal Pattern is necessary. This training data is typically expensive to produce. For this, the understanding of the characteristics of data on network is inevitable. In this paper, we propose to use clustering and association rules as the basis for guiding anomaly detection. For applying entropy to filter noisy data, we present a technique for detecting anomalies without training on normal data. We present dynamic transaction for generating more effectively detection patterns.

Keywords

Intrusions detection; data mining; anomaly detection; misuse detection; training data; detection pattern;

Citations & Related Records

Times Cited By KSCI : 1 (Citation Analysis)

Reference
Cited By KSCI

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KSCI

Anomaly Detection Scheme Using Data Mining Methods 데이터마이닝 기법을 이용한 비정상행위 탐지 방법 연구

Anomaly Detection Scheme Using Data Mining Methods