Abnormal behavior detection technology of power 5G terminal based on the characteristics of service interaction mode

Lu Chen; Nige Li; Mu Chen; Zaojian Dai; Yong Li

doi:10.1117/12.2682787

16 June 2023 Abnormal behavior detection technology of power 5G terminal based on the characteristics of service interaction mode

Lu Chen, Nige Li, Mu Chen, Zaojian Dai, Yong Li

Author Affiliations +

Proceedings Volume 12703, Sixth International Conference on Intelligent Computing, Communication, and Devices (ICCD 2023); 127031C (2023) https://doi.org/10.1117/12.2682787
Event: Sixth International Conference on Intelligent Computing, Communication, and Devices (ICCD 2023), 2023, Hong Kong, China

Abstract

This paper proposes an abnormal behavior detection technology of power 5G terminal based on the characteristics of business interaction mode. First, the abnormal behavior detection framework of power 5G terminal is designed. Secondly, considering various security factors of the basic operating environment, a 5G security monitoring indicator system is established around the terminal side security indicators, network side security indicators and API side security indicators. Then, study the automatic learning and construction technology of normal behavior benchmark model, establish a normal benchmark model based on multiple RBM neural networks, study the normal behavior baseline of 5G terminal operation behavior benchmark, the same type of equipment benchmark, specific communication line benchmark and other normal behavior baselines, and build the security baseline indicators of the normal behavior model of 5G terminal. On this basis, abnormal behavior detection technology based on normal benchmark model is studied.

Citation Download Citation

Lu Chen, Nige Li, Mu Chen, Zaojian Dai, and Yong Li "Abnormal behavior detection technology of power 5G terminal based on the characteristics of service interaction mode", Proc. SPIE 12703, Sixth International Conference on Intelligent Computing, Communication, and Devices (ICCD 2023), 127031C (16 June 2023); https://doi.org/10.1117/12.2682787

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