Network Intrusion Detection Using Self-Recurrent Wavelet Neural Network with Multidimensional Radial Wavelons

In this paper we report a novel application-basedmodel as a suitable alternative for the classification and identification ofattacks on a computer network, and thus guarantee its safety from HTTP protocol-based malicious commands. The proposed model is built on a self-recurrentneural network based on wavelets architecture with multidimensional radialwavelons, and is therefore suited to work online by analyzing non-linearpatterns in real time to self-adjust to changes in its input environment. Sixdifferent neural network based systems have been modeled and simulated forcomparison purposes in terms of overall performance, namely, a feed forwardneural network, an Elman network, a fully connected recurrent neural network, arecurrent neural network based on wavelets, a self-recurrent wavelet networkand the proposed self-recurrent wavelet network with multidimensional radialwavelons. Within the models studied, this paper presents two recurrentarchitectures which use wavelet functions in their functionality in verydistinct ways. The results confirm that recurrent architectures using waveletsobtain superior performance than their peers, in terms not only of theidentification and classification of attacks, but also the speed ofconvergence. DOI: http://dx.doi.org/10.5755/j01.itc.43.4.4626

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