Program Behavior Control System Based on One-class SVM with Adjusted Kernel Using RST
Liu Jin-gang · Jisuanji gongcheng · 2008
Program behavior control system includes program behavior model, detection and response. One-class Support Vector Machine (SVM) has good classification accuracy and generalization in the case of limited samples. In former research of anomaly detection using one-class support vector machine, the attribute weights are not considered. This paper presents a method to introduce weights that reflect attribute importance using Rough Set Theory (RST). The kernel function of one-class support vector machine is adjusted by calculating attribute weights though finding all reducts in the decision system. Experimental results show that the one-class support vector machine with adjusted kernel function using rough set theory has more effective detection capability.