A Monte Carlo method for detecting network traffic in the access layer of power IoT

Zhenglong Leng; Yuanchun Tang; Zhaozheng Zhou

doi:10.1117/12.2674554

28 April 2023 A Monte Carlo method for detecting network traffic in the access layer of power IoT

Zhenglong Leng, Yuanchun Tang, Zhaozheng Zhou

Proceedings Volume 12626, International Conference on Signal Processing, Computer Networks, and Communications (SPCNC 2022); 126261Z (2023) https://doi.org/10.1117/12.2674554
Event: International Conference on Signal Processing, Computer Networks, and Communications (SPCNC 2022), 2022, Zhuhai, China

Abstract

In order to solve the problem of high leakage rate of the access layer network traffic detection method of power IoT, a Monte Carlo method based on the access layer network traffic detection method of power IoT is designed. Combining techniques such as higher-order modulation, improving the power IoT architecture, calculating the degree of randomness of the data blocks obtained quantitatively, extracting access layer features and security requirements, resetting the power window values again, and optimising the network traffic detection model based on Monte Carlo methods. Experimental results: The mean values of the leakage rate of the power IoT access layer network traffic detection method in the paper are: 1.355%, 17.003% and 5.223% for DoS attack, client poisoning and SSH scenarios respectively, indicating that the advantages of the designed power IoT access layer network traffic detection method are more obvious after the full integration of the Monte Carlo method.

Citation Download Citation

Zhenglong Leng, Yuanchun Tang, and Zhaozheng Zhou "A Monte Carlo method for detecting network traffic in the access layer of power IoT", Proc. SPIE 12626, International Conference on Signal Processing, Computer Networks, and Communications (SPCNC 2022), 126261Z (28 April 2023); https://doi.org/10.1117/12.2674554

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