Wireless communication network intrusion path identification based on deep learning algorithms
Due to the different intentions of different types of intrusion attacks,there are also significant differences in the intrusion paths in the network.Therefore,a research on wireless communication network intrusion path identification based on deep learning algorithms is proposed.Based on intrusion intent,attacks with the same behavior are intersected and calculated,and a network intrusion behavior cluster centered on intrusion intent is established.For intrusions with cross relationships,the node distribution probability of network intrusion behavior is used for segmentation.In the stage of intrusion path identification,the grey deep neural network algorithm is introduced to calculate the intrusion intent in the hidden layer.Combining the Euclidean distance between wireless communication network nodes and the node where the current intrusion behavior occurs,the minimum Euclidean distance node corresponding to the cluster is used as the identification result of the next node in the intrusion path.The above operation is repeated to achieve the identification of the overall intrusion path.In the test results,the overall intrusion path coverage nodes did not have an impact on the identification results,and can achieve 100%accurate identification of the intrusion path.
deep learning algorithmswireless communication networkintrusion behavior clusteringgrey deep neural network algorithm
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