A STATIC DETECTION MODEL OF MALICIOUS PDF DOCUMENTS BASED ON NAIVE BAYESIAN CLASSIFIER TECHNOLOGY

被引：0

作者：

Cheng, Huang ^{[1
]}

Yong, Fang ^{[1
]}

Liang, Liu ^{[1
]}

Wang, Lu-Rong ^{[2
]}

机构：

[1] Sichuan Univ, Sch Elect & Informat Engn, Chengdu 610064, Peoples R China

[2] Sichuan Conservatory Mus, Chengdu, Sichuan, Peoples R China

来源：

2012 INTERNATIONAL CONFERENCE ON WAVELET ACTIVE MEDIA TECHNOLOGY AND INFORMATION PROCESSING (LCWAMTIP) | 2012年

关键词：

Static model; malicious document; naive Bayes; heap spray;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

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.

引用

页码：29 / 32

页数：4