Trojan Horse Detection Method Based on Nonlinear SVM Model
Jie Qin · Jisuanji gongcheng · 2011
Aiming at the shortcoming of traditional anti-Trojan technologies,this paper presents the Trojan horse detection method based on nonlinear Support Vector Machine(SVM) model.This method establishes system call sequences in accordance with its system calls function in the system,and converts into SVM readable tags,and places in the data warehouse for SVM extracted as the feature vectors.And to determine the abnormal behavior of testing procedures to determine whether it is Trojan horse by classifying the detected program behaviors based on the SVM classifier.Experimental results show that this method has high accuracy rate,and takes up very little system resource.Besides,it also has a very good performance in detection time and detection of known and unknown Trojan horse attacks.