TY - GEN
T1 - A Security Feature Extraction Method for RF Amplifier Module of 5G Base Station
AU - Hong, Sheng
AU - Xiao, Yuchen
AU - Yin, Hongwei
AU - Yu, Ziyun
N1 - Publisher Copyright:
© 2023 ACM.
PY - 2023/8/25
Y1 - 2023/8/25
N2 - With the increasing popularity of 5G, 5G base stations are facing an increasing number of security threats, and their security status characterization has become a current research hotspot. This paper studies the security feature extraction for RF amplifier modules of 5G base station, providing data support for the security status perception and prediction for RF amplification module of 5G base station,which is one of the core functional modules of 5G base station. A parameter selection method based on correlated information entropy measurement and a feature extraction method based on sparse preserving projection are realized. On this basis, the security sensitive parameter selection set of the RF amplifier module is constructed, and the security features of the RF amplifier module are extracted. The results show that the security feature extraction method for RF amplifier module of 5G base station proposed in this paper can efficiently characterize the security status of 5G base station RF amplifier module.
AB - With the increasing popularity of 5G, 5G base stations are facing an increasing number of security threats, and their security status characterization has become a current research hotspot. This paper studies the security feature extraction for RF amplifier modules of 5G base station, providing data support for the security status perception and prediction for RF amplification module of 5G base station,which is one of the core functional modules of 5G base station. A parameter selection method based on correlated information entropy measurement and a feature extraction method based on sparse preserving projection are realized. On this basis, the security sensitive parameter selection set of the RF amplifier module is constructed, and the security features of the RF amplifier module are extracted. The results show that the security feature extraction method for RF amplifier module of 5G base station proposed in this paper can efficiently characterize the security status of 5G base station RF amplifier module.
UR - https://www.scopus.com/pages/publications/85181396725
U2 - 10.1145/3627341.3630397
DO - 10.1145/3627341.3630397
M3 - 会议稿件
AN - SCOPUS:85181396725
T3 - ACM International Conference Proceeding Series
BT - Proceedings of 2023 International Conference on Computer, Vision and Intelligent Technology, ICCVIT 2023
PB - Association for Computing Machinery
T2 - 2023 International Conference on Computer, Vision and Intelligent Technology, ICCVIT 2023
Y2 - 25 August 2023 through 28 August 2023
ER -