Paper Info
Title
Application of Bayesian belief networks and fuzzy cognitive maps in intrusion analysis.
Abstract
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.
Year
DOI
Venue
2018
10.3233/JIFS-169572
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
Keywords
Field
DocType
Root cause analysis,fuzzy cognitive map,Bayesian belief network,causal reasoning,intrusion analysis
Intrusion,Fuzzy cognitive map,Bayesian network,Artificial intelligence,Machine learning,Mathematics
Journal
Volume
Issue
ISSN
35
1
1064-1246
Citations 
PageRank 
References 
0
0.34
7
Authors
5
Name
Order
Citations
PageRank
Yit Yin Wee1112.28
Wooi Ping Cheah2368.03
Shih Yin Ooi3236.80
Shing Chiang Tan412218.99
Kuokkwee Wee5274.56