A survey of intrusion detection techniques
Computers and Security
Building Probabilistic Networks: 'Where Do the Numbers Come From?' Guest Editors' Introduction
IEEE Transactions on Knowledge and Data Engineering
Network Engineering for Agile Belief Network Models
IEEE Transactions on Knowledge and Data Engineering
Bayesian Event Classification for Intrusion Detection
ACSAC '03 Proceedings of the 19th Annual Computer Security Applications Conference
A Factor Tree Inference Algorithm for Bayesian Networks and Its Application
ICTAI '04 Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence
Using data mining to provide recommendation service
WSEAS Transactions on Information Science and Applications
Robust action strategies to induce desired effects
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
A power-efficient secure routing protocol for wireless sensor networks
WSEAS Transactions on Computers
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This paper describes a structure of a standalone Intrusion Detection System (IDS) based on a large Bayesian network. To implement the IDS we develop the design methodology of large Bayesian networks. A small number of natural templates (idioms) are defined which make the design of Bayesian network easier. They are related to specific fragments of Bayesian networks representing the basic elements in reasoning about uncertain events. The idioms represent the graphical structure, without the probabilistic tables. The use of idioms speeds-up the development of Bayesian networks and improves their quality. Example network is constructed and examined. Such Bayesian network can represent an independent agent in a distributed system. Results are promising since with very limited computation and low sensitivity to the quality of prior knowledge, potentially dangerous situations are successfully detected and classified.