CFAR intrusion detection method based on support vector machine prediction
Di He, Henry Leung · 2005
A novel constant false alarm rate (CFAR) intrusion detection method based on support vector machine (SVM) is proposed in this paper. By introducing the normal network traffic into an SVM neural network, the forthcoming traffic data can be predicted, therefore enhancing the detectability of network attacks. The CFAR threshold of the proposed detector is also derived in the paper theoretically. Computer simulations based on standard DARPA network intrusion data present that the proposed SVM prediction-based approach is superior to other standard intrusion detection method.