On the self-similar nature of Ethernet traffic (extended version)
IEEE/ACM Transactions on Networking (TON)
Wide area traffic: the failure of Poisson modeling
IEEE/ACM Transactions on Networking (TON)
Intrusion detection systems as evidence
Computer Networks: The International Journal of Computer and Telecommunications Networking
IEEE Communications Magazine
The NLAMR network analysis infrastructure
IEEE Communications Magazine
Effective traffic measurement using ntop
IEEE Communications Magazine
Measurement and analysis of IP network usage and behavior
IEEE Communications Magazine
Architectural and technological issues for future optical Internet networks
IEEE Communications Magazine
IP over optical networks: architectural aspects
IEEE Communications Magazine
The Contact Surface: A Technique for Exploring Internet Scale Emergent Behaviors
DIMVA '08 Proceedings of the 5th international conference on Detection of Intrusions and Malware, and Vulnerability Assessment
Wireless network management system for WiMAX / Wi-Fi mesh networks
EUC'07 Proceedings of the 2007 conference on Emerging direction in embedded and ubiquitous computing
Traffic modeling and classification using packet train length and packet train size
IPOM'06 Proceedings of the 6th IEEE international conference on IP Operations and Management
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In this paper, the characteristics of sub-network traffic is analyzed from the correlation point of view. It is easy to see that the traffic histogram of a sub-network has a 24-hour seasonal variation due to daily usage behavior. The auto correlation factor (ACF) and partial auto correlation factor (PACF) tests are applied first to examine the correlation of the traffic among consecutive hours and the correlation with a specific hour. The seasonal auto-regressive integrated moving average (ARIMA) model is applied to characterize the above properties of the network traffic. Modeling performance is evaluated by examining the coincidence of the histogram and the moving average of traffic volume between the actual traffic collected from the network and the traffic generated by the proposed model. The experimental results illustrate that the proposed model can effectively capture traffic behaviors of the sub-network and can then be used as a suitable traffic model for analysis of Internet performance.