Intensive Use of Bayesian Belief Networks for the Unified, Flexible and Adaptable Analysis of Misuses and Anomalies in Network Intrusion Detection and Prevention Systems

Abstract

This paper describes the ESIDE-Depian intrusion detection and prevention system, which uses Bayesian structural and parametric learning and also evidence propagation and adaptation, in order to improve the accuracy and manageability of network intrusion detection systems (NIDS). Current NIDS do not consider the two main detection paradigms, i.e. misuse… (More)
DOI: 10.1109/DEXA.2007.38

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@article{Bringas2007IntensiveUO, title={Intensive Use of Bayesian Belief Networks for the Unified, Flexible and Adaptable Analysis of Misuses and Anomalies in Network Intrusion Detection and Prevention Systems}, author={Pablo Garc{\'i}a Bringas}, journal={18th International Workshop on Database and Expert Systems Applications (DEXA 2007)}, year={2007}, pages={365-371} }