Improved Support Vector Machine for Cyber Attack Detection

This paper presents an efficient and scalable algorithm for classification of cyber attack. The performance of traditional SVM is enhanced in this work by modifying Gaussian kernel to enlarge the spatial resolution around the margin by a conformal mapping, so that the separability between attack classes is increased. It is based on the Riemannian geometrical structure induced by the kernel function. We proposed improved Support Vector Machine (iSVM) algorithm for classification of cyber attack dataset. Result shows that iSVM gives 100% detection accuracy for Normal and Denial of Service (DOS) classes and comparable to false alarm rate, training, and testing times.