Embedded architecture protocol network intrusion detection based on deep residual learning

Rongping Wang

doi:10.1117/12.2655931

12 January 2023 Embedded architecture protocol network intrusion detection based on deep residual learning

Rongping Wang

Proceedings Volume 12509, Third International Conference on Intelligent Computing and Human-Computer Interaction (ICHCI 2022); 125092M (2023) https://doi.org/10.1117/12.2655931
Event: Third International Conference on Intelligent Computing and Human-Computer Interaction (ICHCI 2022), 2022, Guangzhou, China

Abstract

Blockchain Embedded Architecture Protocol network is affected by homomorphic disturbance of nodes in routing and forwarding control, and is vulnerable to embedded intrusion. In order to improve the security of the network, an intrusion detection algorithm based on deep residual learning for Blockchain Embedded Architecture Protocol network is proposed. The statistical feature model of blockchain embedded architecture protocol network intrusion is constructed. The edge information fusion technology of association rules is used to detect and filter the intrusion features of blockchain embedded architecture protocol network, and the spectral features of blockchain embedded architecture protocol network data are extracted. The depth residual learning method is used to carry out the adaptive optimization control in the intrusion detection process of blockchain embedded architecture protocol network, and the multi-threshold depth residual fusion method is used to realize the accurate detection of abnormal traffic data and improve the ability to accurately locate and detect intrusions. The simulation results show that this method has a high accuracy probability, good detection performance and improved network security.

Citation Download Citation

Rongping Wang "Embedded architecture protocol network intrusion detection based on deep residual learning", Proc. SPIE 12509, Third International Conference on Intelligent Computing and Human-Computer Interaction (ICHCI 2022), 125092M (12 January 2023); https://doi.org/10.1117/12.2655931

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KEYWORDS

Network architectures

Computer intrusion detection

Data modeling

Statistical modeling

Computer architecture

Detection and tracking algorithms

Feature extraction

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