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Implementation of Echo State Neural Network and Radial Basis Function Network for Intrusion Detection

Y. Kalpana, S. Purushothaman, R. Rajeswari


Intrusion detection is the art of detecting computer abuse and any attempt to break into networks. As a field of research, it must continuously change and evolve to keep up with new types of attacks or adversaries and the ever-changing environment of the Internet. To make networks more secure, intrusion-detection systems (IDS) aims to recognize attacks. Artificial neural networks (ANN) based IDS were implemented and tested. The goal for using ANNs for intrusion detection is to generalize from incomplete data and able to classify data as being normal or intrusive. An ANN consists of a collection of processing elements that are highly interconnected. Given a set of inputs and a set of desired outputs, the transformation from input to output is determined by the weights associated with the inter-connections among processing elements. By modifying these interconnections, the network adapts to desired outputs. The ability of high tolerance for learning-by-example makes neural networks flexible and powerful in IDS. This paper has implemented Echo state neural network and Radial basis function applied to intrusion detection. The scope of the work includes using the available KDD database.


Radial Basis Function (RBF) Networks Echo State Neural Networks (ESNN), KDD Features, Intrusion Detection.

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