Intrusion detection method based on incremental GHSOM (Growing Hierarchical Self-organizing Maps) neural network
A technology of intrusion detection and neural network, applied in the direction of neural learning method, biological neural network model, electrical components, etc., can solve the problems of inability to detect intrusion behavior in time, change with time, etc., to enhance maturity and reduce space The effect of consumption
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[0042] The present invention will be described in further detail below in conjunction with the accompanying drawings:
[0043] The intrusion detection system of the present invention is composed of two parts: offline training of the neural network model and online detection based on the neural network model. The system collects offline sample data of known attack types from the network as the initial training sample data set for offline training. After obtaining the intrusion detection model, it starts online network intrusion detection. The offline training process uses the traditional GHSOM neural network training algorithm to train the initial neural network model based on the initial training data set. In the online detection process, the GHSOM network model can be dynamically updated by running the incremental GHSOM neural network learning algorithm. Obviously, offline training only initializes the intrusion detection model, and the incremental GHSOM neural network learning...
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