Research on Human Motion Recognition Method Based on Hybrid Convolution Neural Network-Hidden Markov Model

Zhang, Zhen; Zhang, Shirong; Zhao, Zhuanzhe; Liu, Yongming; Kan, Yanpeng

doi:10.1109/icrcv52986.2021.9546966

Cited by 1 publication

(2 citation statements)

References 12 publications

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“…According to the technology of a class of classifcation and the specifc data form of protocol classifcation, we construct a preliminary screening model of a class of classifed protocols and construct a formula (11) from the objective function and constraint conditions of formula 1, 2, 3, 4, and 5. Te constraint ∃II 1 > S1 min ∨I 2 > S2 min ∨I 3 > S3 min ∨I 4 > S4 min ∨I 5 > S5 min is constructed from the decision function of formula (6), thus, the classifcation of protocols is realized. [19] is a kind of network which uses convolution calculation and contains a depth structure.…”

Section: Related Conceptsmentioning

confidence: 99%

“…By extracting feature sequences and their location information, this method avoids the problems caused by the repeatability of feature strings and improves the accuracy of classifcation. Zhang et al [6] designed a convolution neural network model based on deep learning to solve the problem of unknown protocol recognition. Te model has three convolution layers, and the data stream is directly used as the input of the convolution neural network model.…”

Section: Introductionmentioning

confidence: 99%

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Unknown Binary Protocol Recognition Algorithm Based on One Class of Classification and One‐Dimensional CNN

Yin

You

et al. 2023

Mathematical Problems in Engineering

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In order to solve the problem that there are a large number of unknown protocols on the network, which affect the network management and network security in varying degrees, an unknown binary protocol identification method is proposed. On the premise that the center cluster of unknown protocols is obtained by the clustering algorithm, the unknown protocols for network traffic are identified by combining one-class of classification with one-dimensional CNN classification technology. At first, a class of classification algorithm is used to select the unknown protocols, and then, the labeled protocol data obtained by clustering are used to train the one-dimensional CNN model, and the classified binary protocol packets are directly used as the input of the one-dimensional convolution neural network. After the classification of the CNN model, the unknown protocols are finally identified. The experimental results show that the proposed classification and recognition method is better than the traditional CNN and SVM algorithms, and the maximum frequency pooling is better than the traditional pooling method.

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Section: Related Conceptsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%