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

• DOI: 10.5755/j01.itc.43.4.4626
• Publication Date: 2014-12-17T01:38:54Z
• Authors: V. Alarcon-Aquino; J. M. Ramirez-Cortes; P. Gomez-Gil; O. Starostenko; Y. Garcia-Gonzalez
• Keywords: 0209 industrial biotechnology; 02 engineering and technology

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