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Malicious Traffic Detection Using K-means
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 Title & Authors
Malicious Traffic Detection Using K-means
Shin, Dong Hyuk; An, Kwang Kue; Choi, Sung Chune; Choi, Hyoung-Kee;
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 Abstract
Various network attacks such as DDoS(Distributed Denial of service) and orm are one of the biggest problems in the modern society. These attacks reduce the quality of internet service and caused the cyber crime. To solve the above problem, signature based IDS(Intrusion Detection System) has been developed by network vendors. It has a high detection rate by using database of previous attack signatures or known malicious traffic pattern. However, signature based IDS have the fatal weakness that the new types of attacks can not be detected. The reason is signature depend on previous attack signatures. In this paper, we propose a k-means clustering based malicious traffic detection method to complement the problem of signature IDS. In order to demonstrate efficiency of the proposed method, we apply the bayesian theorem.
 Keywords
IDS;K-means;DDoS;Witty Worm;Slammer Worm;
 Language
Korean
 Cited by
1.
Software Defined Networking을 위한 다중 기계학습 결합 기반의 DDoS 탐지 시스템,김영빈;최동호;판 반 트렁;마이 렁;박민호;

한국통신학회논문지, 2017. vol.42. 8, pp.1581-1590 crossref(new window)
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