Conditional mutual information based network intrusion classification method of double-layer semi-idleness Bayesian
A technology of conditional mutual information and Bayesian classifiers, which is applied in data exchange networks, electrical components, digital transmission systems, etc., can solve problems that do not take into account the different independent relations of event attributes of different categories, high time complexity, and inability to Apply real-time intrusion detection system and other issues to achieve the effect of improving intrusion detection performance, reducing false alarms, and improving classification accuracy performance
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[0063] The present invention will be described in detail below in conjunction with the accompanying drawings.
[0064] As shown in Figure 1, the intrusion detection system obtains network message data through the network session event collection device, and performs preprocessing such as message data formatting and feature extraction, and then performs intrusion identification. The results of intrusion identification can continue to carry out alarm correlation, intrusion Tracking and other follow-up processing.
[0065] Intrusion identification is the core step of the network intrusion detection system, and the idea of the present invention is to improve the performance of the entire network intrusion detection system by improving the classification accuracy of the classifier in the intrusion identification. The flow chart of the intrusion identification process, that is, the network intrusion classification method based on the conditional mutual information-based double-lay...
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