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Computer security is essential in information technology world today; confidentiality, availability and integrity of data are the aspects concerned. Firewall has been widely deployed as a protection but it is no longer adequate to against the intelligent intrusions and attacks which keep changing and transforming. A network intrusion detection and analysis system has been introduced in this paper to resolve the problems of data confidentiality, availability and integrity. The challenge of the study is; first, to model the network intrusion detection domain and second, to perform causal reasoning for intrusion detection and analysis based on the domain model constructed earlier. In this paper, a methodology has been proposed to resolve the two problems mentioned above. Both problems will be addressed under causal knowledge driven approach where intrusion detection is viewed as fault diagnosis and prognosis processes. We have proposed Bayesian network for the modeling of network intrusion domain. Also, powerful reasoning capabilities of Bayesian network have been applied to discover intrusion attacks. Since the capabilities of causal reasoning using Bayesian network have not been fully discovered in the domain of intrusion detection by most of the researchers before, this research work is to bridge the gap. From the results of the experiment, we have concluded that the capability of Bayesian learning is reasonably accurate and efficient.

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Intrusion detection is an essential tool to protect hacking and unauthorized access in computer networks nowadays. Mechanisms used to attack keep evolving as the internet technology is improving. Hence, the task of differentiating authorized and unauthorized access has become more and more challenging. The modeling of network intrusion domain and causal reasoning for the intrusion detection has been proposed in this paper to address the security issues of a network. Bayesian network modeling with causal knowledge-driven approach has been selected for a network intrusion domain. Reasoning capabilities of Bayesian network have been adapted to perform detection and analysis in the domain.There are two main problems to be addressed in this paper: the first problem is to model the network intrusion domain and the second problem is to perform causal reasoning for intrusion detection and analysis. A methodology has been proposed to solve the two problems mentioned above. Intrusion detection is viewed as fault diagnosis in causal reasoning, and the analysis of the effect is viewed as fault prognosis. To address the first problem under causal knowledge-driven approach, we propose Bayesian network for the modeling of network intrusion domain. The second problem is addressed by applying the powerful reasoning capabilities of Bayesian network. The capabilities of causal reasoning using Bayesian network have not been fully discovered in the domain of intrusion detection. This research work is to bridge the gap. respectively. Currently, he is a senior lecturer with the Faculty of Information Science and Technology, Melaka campus, Multimedia University, Malaysia. His research interests include computational intelligence techniques (artificial neural networks, evolutionary algorithms, decision trees, etc) and their applications to pattern classification, condition monitoring, fault diagnosis and medical diagnosis.

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