Abnormal detecting method based on fuzzy nervous network

A fuzzy neural network and anomaly detection technology, applied in data exchange network, data exchange through path configuration, digital transmission system, etc., can solve inflexibility and other problems, achieve the effect of reducing dimensionality and improving operating efficiency

Inactive Publication Date: 2007-10-10
SUN YAT SEN UNIV
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Problems solved by technology

[0006] 2. The output value set classification problem in the detection stage
In the application of CN1555156, a threshold-based discrimination method is adopted, which is extremely inflexible

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  • Abnormal detecting method based on fuzzy nervous network

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Embodiment Construction

[0024] The present invention will be further elaborated below in conjunction with the accompanying drawings.

[0025] As shown in Figure 1, the anomaly detection method based on fuzzy neural network mainly includes two stages: training stage and detection stage. In the training stage, firstly, the input network connection vector is obtained from the network connection data vector training sample set, and then the feature vector is generated after feature selection and feature transformation, and then the feature vector is sent to the fuzzy neural network, using ANFIS, (Adaptive Fuzzy Neural Network Inference system) is trained until it reaches a stable state, and the fuzzy neural network model is obtained. In the detection stage, firstly, the input network connection vector is obtained from the network connection data vector training sample set, which is preprocessed to generate a feature vector, and then the feature vector is sent to the trained fuzzy neural network model to ...

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Abstract

The method comprises two stages -a training stage and a test stage. The training stage comprises: getting the connection vector of the input network from the network connection data vector training sample set; making a feature selection and a feature conversion for it to generate a feature vector; sending the feature vector to the fuzzy neural network; using ANFIS to make training, and until it is stabilized to get the fuzzy neutral network model. The test stage comprises: in the first, getting the network connection vector from the network connection data vector training sampling set; after making pre-process, generating a feature vector; inputting the feature vector into the trained fuzzy neural network to get relevant output value; finally, making the fuzzy clustering for the output value set.

Description

technical field [0001] The invention relates to an anomaly detection method aimed at network intrusion, belonging to the technical field of computer network security. Background technique [0002] Network anomalies mainly refer to situations where the network environment is different from normal network behavior, and can be broadly divided into two categories: the first category is related to network failures (such as node and link failures) and administrator misoperations, and the second category is The category is related to network security issues. One of the main threats to network security is the attack and destruction of the network and the intrusion of the information system through the network. A network intrusion can be defined as: any collection of network activities that attempt to compromise the integrity, confidentiality, or availability of an information system. [0003] The traditional network intrusion detection method is misuse detection, which can accurate...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H04L12/26H04L12/24H04L29/06H04L12/28
Inventor 何海涛罗笑南
Owner SUN YAT SEN UNIV
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