Self-adaptive semi-supervised network traffic classification method, system and equipment
An adaptive network and traffic classification technology, applied in the field of self-adaptive semi-supervised network traffic classification, can solve problems such as the inability to realize system parameter adaptation and the inability to automatically determine the best parameters, so as to improve the classification accuracy and ensure reliability. and accuracy, high purity effect
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Embodiment 8
[0082] Such as image 3 As shown, the present invention also provides an adaptive semi-supervised network traffic classification system, which includes:
[0083] Acquisition module, vector set processing module, clustering module, classification module, output module;
[0084] The obtaining module is used to obtain marked and unmarked network flows, extract a preset fixed amount of flow characteristics in each network flow, and obtain network flow feature vectors;
[0085] The vector set processing module is used to calculate the centroid of the network flow feature vector set in each type according to the marked network flow feature vector, and obtain the vector set M;
[0086] The clustering module is used to use the vector set M as the initial center point of k-means clustering, and perform adaptive semi-supervised k- means clustering, and output k-means clustering;
[0087] The classification module is configured to map the obtained network flows in each type of cluster...
Embodiment 10
[0094] Such as Figure 4 As shown, the embodiment of the present invention also provides a computer device, which includes: a processor, a memory, and a computer program stored on the memory and operable on the processor, wherein the processing When the program is executed by the computer, the steps of the method described in any one of the foregoing embodiments 1 to 7 are implemented.
[0095] It should be noted that, in this embodiment 10, the computer equipment of the present invention is used to obtain marked and unmarked network flows, extract a preset fixed amount of flow features in each network flow, and obtain network flow feature vectors, According to the marked network flow feature vector, calculate the center point of each type of network flow that has been marked, and use the center point as the initial clustering center M of the k-means algorithm, for the mixed marked types and Semi-supervised k-means clustering is performed on the unlabeled network flow point s...
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