Probabilistic inference strategy in distributed intrusion detection systems

被引：0
|
作者：
Ding, JG [1 ]
Xu, SH
Krämer, B
Bai, YC
Chen, HS
Zhang, J
机构：
[1] Shanghai Jiao Tong Univ, Shanghai 200030, Peoples R China
[2] Fern Univ Hagen, D-58084 Hagen, Germany
来源：
PARALLEL AND DISTRIBUTED PROCESSING AND APPLICATIONS, PROCEEDINGS | 2004年 / 3358卷
关键词：
D O I：
暂无
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
The level of seriousness and sophistication of recent. cyber-attacks has risen dramatically over the past decade. This brings great challenges for network protection and the automatic security management. Quick and exact localization of intruder by an efficient intrusion detection system (IDS) will be great helpful to network manager. In this paper, Bayesian networks (BNs) are proposed to model the distributed intrusion detection based on the characteristic of intruders' behaviors. An inference strategy based on BNs are developed, which can be used to track the strongest causes (attack source) and trace the strongest dependency routes among the behavior sequences of intruders. This proposed algorithm can be the foundation for further intelligent decision in distributed intrusion detection.
引用
收藏
页码：835 / 844
页数：10
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