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首页> 外文期刊>Journal of intelligent & fuzzy systems: Applications in Engineering and Technology >Application of Bayesian belief networks and fuzzy cognitive maps in intrusion analysis
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Application of Bayesian belief networks and fuzzy cognitive maps in intrusion analysis

机译：贝叶斯信仰网络和模糊认知地图在入侵分析中的应用

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摘要

Bayesian belief networks (BBN) and fuzzy cognitive maps (FCM) are two major causal knowledge frameworks that are frequently used in various domains for cause and effect analysis. However, most researchers use these as separate approaches to analyse the cause(s) and effect(s) of an event. In practice, both methods have their own strengths and weaknesses in both causal modelling and causal analysis. In this paper, a combination of BBN and FCM is used in order to model and analyse network intrusions. First, the BBN is learnt from network intrusion data; following this, an FCM is generated from the BBN, using a migration method. A data-mining approach is suitable for use in the construction of a BBN for network intrusion since this is a data-rich domain, while an FCM is appropriate for the intuitive representation of complex domains. The proposed method of network intrusion analysis using both BBN and FCM consists of several stages, in order to leverage the capabilities of each approach in building the causal model and performing causal analysis. Both the intuitive representation of the causal model in FCM and the wide variety of reasoning methods supported by BBN are exploited in this research to facilitate network intrusion analysis.
机译：贝叶斯信仰网络（BBN）和模糊认知地图（FCM）是两种主要因果知识框架，经常用于原因和效果分析的各个领域。然而，大多数研究人员使用这些方法作为分析事件的原因和效果的单独方法。在实践中，两种方法都有自己的因果建模和因果分析中的自己的优点和缺点。在本文中，使用BBN和FCM的组合来模拟和分析网络入侵。首先，BBN从网络入侵数据中学到;在此之后，使用迁移方法从BBN生成FCM。数据挖掘方法适用于建造BBN以进行网络入侵，因为这是一种数据丰富的域，而FCM适用于复杂域的直观表示。使用BBN和FCM的网络入侵分析方法包括多个阶段，以利用每种方法的能力建立因果模型和进行因果分析。在该研究中利用了FCM中因果模型和BBN支持的各种推理方法的直观表示，以便于网络入侵分析。

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