A static detection model of malicious PDF documents based on naive Bayesian classifier technology
Huang Cheng, Fang Yong, Liang Liu, Wang Lu-rong · 2012
For the purpose of improving native detective method based on signature matching of traditional anti-virus software and inadequate performance of dynamic testing, the researchers demonstrate a new static detection model of malicious PDF documents based on naive Bayes classifier technology. The model considers with exploit techniques of heap spray, JavaScript syntax and shellcode feature. Compare to traditional detection techniques, the training samples and actual test data showed that the detection efficiency and accuracy of the model have improved greatly.