Using Length-weighted Once Kernel to Detect Anomalous Process

In this paper, we present a new string kernel, called length-weighted once kernel, and propose an efficient algorithm to compute this kernel. The algorithm is based on dynamic programming and suffix kernel. Moreover, we intend to distinguish anomalous process from normal processes using a one-class support vector machine classifier with certain kernel function. In the experiments, gap-weighted kernels, length-weighted once kernel, and RBF kernel are tested with an SVM classifier on the UNM datasets. The experimental results reveal that the length- weighted once kernel outperforms the others.