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首页> 外文会议>Intelligence and Security Informatics, 2009. ISI '09 >Intrusion detection based on “Hybrid” propagation in Bayesian Networks
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Intrusion detection based on “Hybrid” propagation in Bayesian Networks

机译：贝叶斯网络中基于“混合”传播的入侵检测

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摘要

The goal of a network-based intrusion detection system (IDS) is to identify malicious behaviour that targets a network and its resources. Intrusion detection parameters are numerous and in many cases they present uncertain and imprecise causal relationships which can affect attack types. A Bayesian Network (BN) is known as graphical modeling tool used to model decision problems containing uncertainty. In this paper, a BN is used to build automatic intrusion detection system based on signature recognition. A major difficulty of this system is that the uncertainty on parameters can have two origins. The first source of uncertainty comes from the uncertain character of information due to a natural variability resulting from stochastic phenomena. The second source of uncertainty is related to the imprecise and incomplete character of information due to a lack of knowledge. The goal of this work is to propose a method to propagate both the stochastic and the epistemic uncertainties, coming respectively from the uncertain and imprecise character of information, through the Bayesian model, in an intrusion detection context.
机译：基于网络的入侵检测系统（IDS）的目标是识别针对网络及其资源的恶意行为。入侵检测参数很多，在许多情况下，它们表现出不确定的和不精确的因果关系，这些关系可能会影响攻击类型。贝叶斯网络（BN）被称为图形建模工具，用于对包含不确定性的决策问题进行建模。本文使用BN来构建基于签名识别的自动入侵检测系统。该系统的主要困难在于参数的不确定性可能有两个来源。不确定性的第一个来源来自信息的不确定性，这是由于随机现象导致的自然可变性。不确定性的第二个来源与由于缺乏知识导致信息的不精确和不完整有关。这项工作的目的是提出一种在入侵检测环境中通过贝叶斯模型传播随机不确定性和认知不确定性的方法，该不确定性分别来自信息的不确定性和不精确性。

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