With the development of the short message services,the characteristics and contents of the spam short Message are also changing constantly,the main problems that exist in the traditional short message filtering systems are that the characteristics and contents fail to be updated in time,which reduced the filter capability.This paper mainly utilized Na ve Bays advantage of rapid statistics classification and Support Vector Machine(SVM)incremental training characteristic in Spam Short Message filtering,and provided feedbacks to the online filtering sub-system in time in order to enhance the system-s self-adaptability.The experimental results show that this new method effectively deals with the above problems in the traditional spam short Message filtering systems.

In this paper, we discuss research advances in machine learning, data mining, statistical learning theory and support vector machine, and briefly introduce some novel artificial intelligence (AI) techniques that have just been presented in recent years, such as probabilistic graphical models, Markov logic networks etc. In the meantime, according to the requirements and characteristics of information countermeasure, we discuss some possible applications of the above novel AI techniques in information countermeasure.