Application of Online SVM in real-time intrusion detection
Online SVM在实时入侵检测中的应用研究

As a new classification method, Online Support Vector Machines (Online SVM) can offer superior classification performance for anomaly intrusion detection. The conventional SVM, Robust SVM and one-class SVM have been modified respectively based on the idea from Online SVM in this paper, and their performances have been compared with that of the original algorithms. After elaborate theoretical analysis, concrete experiments with 1999 DARPA data set collected at MIT's Lincoln Labs were carried out. These experiments verify that the modified SVMs can be trained online and the results outperform the original ones with fewer Support Vectors (SVs) and less training time without decreasing detection accuracy in the presence of noise.