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A distributed intrusion detection system based on bayesian alarm networks

机译:基于贝叶斯警报网络的分布式入侵检测系统

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Intrusion Detection in large network must rely on use of many distributed agents instead to one large monolithic module. Agents should have some kind of artificial intelligence in order to cope successfully with different intrusion problems. In this paper, we suggested Bayesian alarm network to work as independent Network Intrusion Detection Agent. We have shown that when narrowed in detecting one specific type of the attack in large network, for example denial of service, virus, worm or privacy attack, we can induce much more prior knowledge into system regarding the attack. Different nodes of the network can develop their own model of Bayesian alarm network and agents could communicate between themselves and with common security data base. Networks should be organized hierarchically so on the higher level of hierarchy, Bayesian alarm network, thanks to interconnections with lower level networks and data, acts as a distributed Intrusion Detection System.
机译:大型网络中的入侵检测必须依靠使用许多分布式代理,而不是一个大型的整体模块。代理程序应具有某种人工智能,以便成功应对各种入侵问题。在本文中,我们建议将贝叶斯警报网络用作独立的网络入侵检测代理。我们已经显示出,在检测大型网络中一种特定类型的攻击(例如拒绝服务,病毒,蠕虫或隐私攻击)时,我们可以将更多有关攻击的先验知识带入系统。网络的不同节点可以开发自己的贝叶斯警报网络模型,并且代理可以在它们之间以及与通用安全数据库进行通信。网络应按层次进行组织,因此,由于与较低级别的网络和数据互连,因此较高层次的贝叶斯警报网络可充当分布式入侵检测系统。

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