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공공누리This item is licensed Korea Open Government License

Title
Toward a more practical unsupervised anomaly detection system
Author(s)
송중석
Publication Year
2013-05-10
Abstract
During the last decade, various machine learning and data mining techniques have been applied to Intrusion Detection Systems (IDSs) which have played an important role in defending critical computer systems and networks from cyber attacks. Unsupervised anomaly detection techniques have received a particularly great amount of attention because they enable construction of intrusion detection models without using labeled training data (i.e., with instances preclassified as being or not being an attack) in an automated manner and offer intrinsic ability to detect unknown attacks, i.e., 0-day attacks. Despite the advantages, it is still not easy to deploy them into a real network environment because they require several parameters during their building process, and thus IDS operators and managers suffer from tuning and optimizing the required parameters based on changes of their network characteristics. In this paper, we propose a new anomaly detection method by which we can automatically tune and optimize the values of parameters without predefining them. We evaluated the proposed method over real traffic data obtained from Kyoto University honeypots. The experimental results show that the performance of the proposed method is superior to that of the previous one.
Keyword
IDS; Clustering; Anomaly Detection
Journal Title
Information sciences
ISSN
0020-0255
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Appears in Collections:
7. KISTI 연구성과 > 학술지 발표논문
URI
https://repository.kisti.re.kr/handle/10580/14238
http://www.ndsl.kr/ndsl/search/detail/article/articleSearchResultDetail.do?cn=NART65523770
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