首页 | 本学科首页   官方微博 | 高级检索  
     检索      

Markov Graph Model Computation and Its Application to Intrusion Detection
作者姓名:曾剑平  郭东辉
作者单位:Department of Physics, Xiamen University, Xiamen, Fujian 361005, China
摘    要:Markov model is usually selected as the base model of user action in the intrusion detection system (IDS). However, the performance of the IDS depends on the status space of Markov model and it will degrade as the space dimension grows. Here, Markov Graph Model (MGM) is proposed to handle this issue. Specification of the model is described, and several methods for probability computation with MGM are also presented. Based on MGM, algorithms for building user model and predicting user action are presented. And the performance of these algorithms such as computing complexity, prediction accuracy, and storage requirement of MGM are analyzed.

关 键 词:概率计算  网络安全  病毒侵入  计算机技术
文章编号:1672-5220(2007)02-0272-04
修稿时间:2006-08-20

Markov Graph Model Computation and Its Application to Intrusion Detection
ZENG Jian-ping,GUO Dong-hui.Markov Graph Model Computation and Its Application to Intrusion Detection[J].Journal of Donghua University,2007,24(2):272-275.
Authors:ZENG Jian-ping  GUO Dong-hui
Institution:Department of Physics, Xiamen University, Xiamen, Fujian 361005, China
Abstract:Markov model is usually selected as the base model of user action in the intrusion detection system (IDS). However, the performance of the IDS depends on the status space of Markov model and it will degrade as the space dimension grows. Here, Markov Graph Model (MGM) is proposed to handle this issue. Specification of the model is described, and several methods for probability computation with MGM are also presented. Based on MGM,algorithms for building user model and predicting user action are presented. And the performance of these algorithms such as computing complexity, prediction accuracy, and storage requirement of MGM are analyzed.
Keywords:Markov Graph Model  intrusion detection  probability computation
本文献已被 维普 万方数据 等数据库收录!
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号