Antagonism Comparison of Linear Classifier in Adversarial Environment
Pei Xiao-hui · Computer and Modernization · 2012
Machine learning algorithms provide a well solution for many security applications.Machine learning algorithms themselves,however,face the thread of adversary attack.In order to analyze the impact of adversary attacks on machine learning algorithms,the paper presents an adversary attack model in line with some actual situations and compares the antagonism of some linear classifier under this model.The performances of these adversarial classifiers are evaluated on a large public spam corpus.The experiment results show that SVM is more antagonism than other linear classifiers.