A Study on Traffic Anomaly Detection Scheme Based Time Series Model 


Vol. 33,  No. 5, pp. 304-309, May  2008


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  Abstract

This paper propose the traffic anomaly detection scheme based time series model. We apply ARIMA prediction model to this scheme and transform the value of the abnormal symptom into the probability value to maximize the traffic anomaly symptom detection. For this, we have evaluated the abnormal detection performance for the proposed model using total traffic and web traffic included the attack traffic. We will expect to have an great effect if this scheme is included in some network based intrusion detection system.

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  Cite this article

[IEEE Style]

K. H. Cho and D. H. Lee, "A Study on Traffic Anomaly Detection Scheme Based Time Series Model," The Journal of Korean Institute of Communications and Information Sciences, vol. 33, no. 5, pp. 304-309, 2008. DOI: .

[ACM Style]

Kang Hong Cho and Do Hoon Lee. 2008. A Study on Traffic Anomaly Detection Scheme Based Time Series Model. The Journal of Korean Institute of Communications and Information Sciences, 33, 5, (2008), 304-309. DOI: .

[KICS Style]

Kang Hong Cho and Do Hoon Lee, "A Study on Traffic Anomaly Detection Scheme Based Time Series Model," The Journal of Korean Institute of Communications and Information Sciences, vol. 33, no. 5, pp. 304-309, 5. 2008.

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pISSN : 1226 - 4717
eISSN : 2287 - 3880