Intrusion Detection Systems Using Adaptive Regression Splines
نویسندگان
چکیده
Past few years have witnessed a growing recognition of soft computing technologies for the construction of intelligent and reliable intrusion detection systems. Due to increasing incidents of cyber attacks, building effective intrusion detection systems (IDSs) are essential for protecting information systems security, and yet it remains an elusive goal and a great challenge. In this paper, we report a performance analysis between Multivariate Adaptive Regression Splines (MARS), neural networks and support vector machines. The MARS procedure builds flexible regression models by fitting separate splines to distinct intervals of the predictor variables. A brief comparison of different neural network learning algorithms is also given.
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