Malicious Traffic Detection Using K-means 


Vol. 41,  No. 2, pp. 277-284, Feb.  2016


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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.

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  Cite this article

[IEEE Style]

D. H. Shin, K. K. An, S. C. Choi, H. Choi, "Malicious Traffic Detection Using K-means," The Journal of Korean Institute of Communications and Information Sciences, vol. 41, no. 2, pp. 277-284, 2016. DOI: .

[ACM Style]

Dong Hyuk Shin, Kwang Kue An, Sung Chune Choi, and Hyoung-Kee Choi. 2016. Malicious Traffic Detection Using K-means. The Journal of Korean Institute of Communications and Information Sciences, 41, 2, (2016), 277-284. DOI: .

[KICS Style]

Dong Hyuk Shin, Kwang Kue An, Sung Chune Choi, Hyoung-Kee Choi, "Malicious Traffic Detection Using K-means," The Journal of Korean Institute of Communications and Information Sciences, vol. 41, no. 2, pp. 277-284, 2. 2016.

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pISSN : 1226 - 4717
eISSN : 2287 - 3880