Abstract
The network security is the process of preventing and detecting unauthorized use of the networks and stopping unauthorized users from accessing any part of systems. So, the network monitoring is demanding task. It is an essential part in the use of network administrators who are trying to maintain the good operating of their networks and need to monitor the traffic movements and the network performances. The needs of data analysis and classification are significantly increased to categorize data and make decisions by implementation of standardization and information extraction techniques. The design of an efficient classifier is one of the fundamental issues of automatic training. The actual network monitoring systems suffer from many constraints at the level of analysis and classification of data. To overcome this problem, it was necessary to define reliable methods of analysis to implement a relevant system to monitor the circulated traffic. The main goal of this paper is to model and validate a new traffic classifier able to categorize the collected data within the networks. This new proposed model is based on multilayer perceptron that composed of three layers. An efficient training algorithm is proposed to optimize the weights but also a recognition algorithm to validate the model.
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Guezzaz, A., Asimi, A., Mourade, A., Tbatou, Z., Asimi, Y. (2020). A Multilayer Perceptron Classifier for Monitoring Network Traffic. In: Farhaoui, Y. (eds) Big Data and Networks Technologies. BDNT 2019. Lecture Notes in Networks and Systems, vol 81. Springer, Cham. https://doi.org/10.1007/978-3-030-23672-4_19
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DOI: https://doi.org/10.1007/978-3-030-23672-4_19
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