یک روش برچسب زدن مستقل به الگوریتم های ماشین آلات بردار پشتیبانی برای شناسایی ناهنجاری ترافیک شبکه

This work presents an approach for autonomous labeling of normal traffic as a way of dealing with situations where class distribution does not present the imbalance required for SVM algorithms. In this case, the autonomous labeling process is made by SNORT, a misuse-based intrusion detection system. Experiments conducted on the 1998 DARPA dataset show that the use of the proposed autonomous labeling approach not only outperforms existing SVM alternatives but also, under some attack distributions, obtains improvements over SNORT itself.