ANOVA Decision Tool for Determining the Best Estimation of a Large Scale IP Network Traffic Matrix Using ARIMA/GARCH Algorithm

2018 ◽  
pp. 69-79
Author(s):  
Slimane Mekaoui ◽  
Anis Boubekeur ◽  
Choukri Benhamed ◽  
Kamal Ghoumid
Keyword(s):  
Network Traffic ◽  
Large Scale ◽  
Decision Tool ◽  
Traffic Matrix ◽  
Ip Network

scholarly journals Deep learning based network traffic matrix prediction

2021 ◽  
Vol 2 ◽  
pp. 46-56
Author(s):  
Dalal Aloraifan ◽  
Imtiaz Ahmad ◽  
Ebrahim Alrashed
Keyword(s):  
Deep Learning ◽  
Network Traffic ◽  
Traffic Matrix

scholarly journals Probability-model based network traffic matrix estimation

2014 ◽  
Vol 11 (1) ◽  
pp. 309-320
Author(s):  
Hui Tian ◽  
Yingpeng Sang ◽  
Hong Shen ◽  
Chunyue Zhou
Keyword(s):  
Large Scale ◽  
Probability Model ◽  
Estimation Methods ◽  
Matrix Estimation ◽  
Large Scale Network ◽  
Traffic Matrix ◽  
Minimization Methods ◽  
Traffic Matrix Estimation ◽  
Scale Network ◽  
Mean Square Errors

Traffic matrix is of great help in many network applications. However, it is very difficult to estimate the traffic matrix for a large-scale network. This is because the estimation problem from limited link measurements is highly underconstrained. We propose a simple probability model for a large-scale practical network. The probability model is then generalized to a general model by including random traffic data. Traffic matrix estimation is then conducted under these two models by two minimization methods. It is shown that the Normalized Root Mean Square Errors of these estimates under our model assumption are very small. For a large-scale network, the traffic matrix estimation methods also perform well. The comparison of two minimization methods shown in the simulation results complies with the analysis.

scholarly journals Intrusion Dataset Over Network Traffic of SDN and TCP/IP Network

2021 ◽  
pp. 694-701
Author(s):  
Karan Shingare ◽  
Rohit Nandurkar ◽  
Prashant Shrivastav ◽  
Shailesh Bendale
Keyword(s):  
Intrusion Detection ◽  
Network Topology ◽  
Network Traffic ◽  
Intrusion Detection System ◽  
Detection System ◽  
Host System ◽  
Ip Network ◽  
The World

As the world is moving toward newer technologies and to meet the requirements of the same adapting toward different network topology. SDN is such example of a network which solves many issues or limitations of a traditional TCP/IP network. As majority of workspace is moving towards SDN, many new vulnerabilities are also emerging, and to protect the network and systems on these networks, in this paper we discuss and propose a dataset which would be helpful in training an intrusion detection system over SDN which would also include the intrusion dataset for traditional TCP/IP network too. We generate this data over SDN topology by attacking the host system present in the network, then analyse the generated data using CICFlowmeter which would give us the desired dataset for intrusion detection.

Generic IP Network Traffic Management from Measurement through Analyses to Simulation

2014 ◽  
pp. 167-192 ◽  
Author(s):  
Seferin Mirtchev ◽  
Constandinos X. Mavromoustakis ◽  
Rossitza Goleva ◽  
Kiril Kassev ◽  
George Mastorakis
Keyword(s):  
Network Traffic ◽  
Traffic Management ◽  
Ip Network
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