Network traffic forecast and anomaly detection based on time series models
碩士 === 國立屏東科技大學 === 工業管理系所 === 101 === The varieties of network applications provide convenient services to users and create many commerce markets. However, lots of network hacking activities have been attacking the services and cause extensive damage and inconvenience. It is very important for netw...
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| Format: | Others |
| Language: | zh-TW |
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2013
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| Online Access: | http://ndltd.ncl.edu.tw/handle/08527912095885306343 |
| Summary: | 碩士 === 國立屏東科技大學 === 工業管理系所 === 101 === The varieties of network applications provide convenient services to users and create many commerce markets. However, lots of network hacking activities have been attacking the services and cause extensive damage and inconvenience. It is very important for network managers to protect the services and improve the QoS and the security. To create an efficient network abnormal detecting system, we need to collect and analyze the network activities. In this paper we collect network traffic data from school of information at MCU. The dataset are stored in Netflow format and dated from 2012/05/15 to 2012/06/22. The rescaled range (R/S) analysis method is used to compute the Hurst index to verify the property of long memory and estimate the fractional difference order. The R statistical package is then adopted to build the seasonal autoregressive fractional integrated moving average model to establish 95% prediction interval. The results of this study
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are able to provide the control limit for monitoring network traffic.
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