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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. [ABSTRACT FROM AUTHOR]

Data mining and machine learning methods have been utilized successfully in the past for identifying and forecasting meaningful patterns from data repositories of diverse application domains. However, the high number of dimensions and instances present in large datasets pose great technical challenges to these existing methods of classification and prediction. The presence of noisy data and missing values makes it even tougher to achieve accurate prediction outcomes. A number of hybrid methodologies constituting dimensionality reduction, feature selection and noise removal methods have been proposed in the literature. However, majority of these techniques force the analysts to compromise on accuracy of classification and prediction results. Therefore, there is a strong need of a methodology that not only scales well with the sheer size and volume of data but also provides near to accurate classification and prediction results by effectively handling the noise in data variables. This paper proposes a fuzzy-based methodology which ranks the dimensions in order of importance and exploits Fuzzy Nearest Neighbor (FNN) approaches for accurate classification and prediction. An experimental evaluation on real world datasets, taken from UCI machine learning repository, shows that the proposed approach outperforms the existing classification and prediction methods by employing only a subset of important features to achieve high prediction accuracy rates at multiple levels of data abstraction. [ABSTRACT FROM AUTHOR]