Intrusion signal discrimination method based on MFCC-energy entropy feature and FTO-SVM

Hepu Chen; Huaming Wu; Yechao Zhang; Wenbo Xiao; Yongsheng Xiao; Lizhen Huang; Jie Zeng

doi:10.1117/12.2638614

23 May 2022 Intrusion signal discrimination method based on MFCC-energy entropy feature and FTO-SVM

Hepu Chen, Huaming Wu, Yechao Zhang, Wenbo Xiao, Yongsheng Xiao, Lizhen Huang, Jie Zeng

Proceedings Volume 12254, International Conference on Electronic Information Technology (EIT 2022); 122541I (2022) https://doi.org/10.1117/12.2638614
Event: International Conference on Electronic Information Technology (EIT 2022), 2022, Chengdu, China

Abstract

Aiming at the problem that Distributed Optical Fiber Acoustic Sensing (DAS) system will misjudge external intrusion signals, an intrusion signal discrimination method based on MFCC-Energy entropy feature and FTO-SVM is proposed. Firstly, the former 13-dimensional Mel-Frequency Cepstral Coefficients (MFCC) is extracted from the collected sound signal, and Principal Component Analysis (PCA) is used to reduce the dimension of MFCC; Secondly, the energy entropy of the collected sound signal is calculated, which fused with the reduced MFCC as the feature parameter of the collected sound signal; Finally, the extracted feature parameters are discriminated by using Support Vector Machine (SVM) with the hyperparameters optimized by the Fibonacci Tree Optimization (FTO) algorithm. The results show that the proposed method can effectively improve the system discrimination accuracy for intrusion signals, and is of great significance to perimeter security and fault diagnosis and other related fields.

Citation Download Citation

Hepu Chen, Huaming Wu, Yechao Zhang, Wenbo Xiao, Yongsheng Xiao, Lizhen Huang, and Jie Zeng "Intrusion signal discrimination method based on MFCC-energy entropy feature and FTO-SVM", Proc. SPIE 12254, International Conference on Electronic Information Technology (EIT 2022), 122541I (23 May 2022); https://doi.org/10.1117/12.2638614

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