Abstract
This paper presents an application-based model for classifying and identifying attacks in a communications network and therefore guarantees its safety from HTTP protocol-based malicious commands. The proposed model is based on a recurrent neural network architecture and it is therefore suitable to work online and for analyzing non-linear patterns in real time to self-adjust to changes in its input environment. Three different neural network-based systems have been modelled and simulated for comparison purposes in terms of overall performance: a Feed-forward Neural Network, an Elman Network, and a Recurrent Neural Network. Simulation results show that the latter possesses a greater capacity than either of the others for the correct identification and classification of HTTP attacks, and it also reaches a result at a great speed, its somewhat taxing computing requirements notwithstanding.
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Alarcon-Aquino, V., Oropeza-Clavel, C.A., Rodriguez-Asomoza, J., Starostenko, O., Rosas-Romero, R. (2010). Intrusion Detection and Classification of Attacks in High-Level Network Protocols Using Recurrent Neural Networks. In: Sobh, T., Elleithy, K., Mahmood, A. (eds) Novel Algorithms and Techniques in Telecommunications and Networking. Springer, Dordrecht. https://doi.org/10.1007/978-90-481-3662-9_21
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DOI: https://doi.org/10.1007/978-90-481-3662-9_21
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