Polynomial Discriminant Radial Basis Function for Intrusion Detection

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Executive Summary

With the immense growth of network-based services and sensitive information on networks, network security plays an important role. Intrusion poses a serious security risk in a network environment. An Intrusion Detection System (IDS) inspects the activities in a system for suspicious behavior or patterns that may indicate system attack or misuse. Artificial neural networks provide the potential to identify and classify network activity based on limited, incomplete, and nonlinear data sources. Among several neural network techniques, polynomial discriminant Radial Basis Function (PRBF) has incorporated the system to achieve robustness and flexibility. Based on several models with different measures, PRBF makes the final decision of whether current behavior is abnormal or not.

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