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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赖于许多分布式代理的使用,而不是一个大型单片模块。代理人应该有某种人工智能,以便成功地应对不同的入侵问题。在本文中,我们建议贝叶斯警报网络作为独立网络入侵检测代理。我们已经表明,当缩小在大型网络中检测到一种特定类型的攻击时,例如拒绝服务,病毒,蠕虫或隐私攻击,我们可以诱导更高的知识进入有关攻击的系统。网络的不同节点可以开发自己的贝叶斯警报网络和代理模型,可以在自己和共同的安全数据库之间进行通信。由于具有较低级别网络和数据的互连,因此应在更高层次的层次结构,贝叶斯警报网络上进行分层组织地进行分层组织,这是分布式入侵检测系统的互连。

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