光纤传感网络混合式入侵行为实时检测研究  

Research on real time detection of hybrid intrusion behavior in fiber optic sensor networks

作　　者：陆思辰[1] 王福军 LU Sichen;WANG Fujun(Boda College of Jilin Normal University,Siping Jilin 136000,China;Jilin Normal University,Siping Jilin 136000,China)

机构地区：[1]吉林师范大学博达学院,吉林四平136000 [2]吉林师范大学,吉林四平136000

出　　处：《激光杂志》2025年第1期202-207,共6页Laser Journal

基　　金：国家教育部就业育人实习基地项目(No.20230109928);吉林省高教科研课题项目(No.JGJX24B58)。

摘　　要：混合式入侵行为往往在一个或多个局部位置出现,且在时间上存在一定的聚集性,无法很好地捕捉其复杂特征,为此提出光纤传感网络混合式入侵行为实时检测方法。以平均过零率和短时能量作为指标对某段信号进行分割处理,减少不断累加的处理延时,提取可能存在入侵行为的光纤传感信号。通过高阶谱分析、样本熵分析和奇异值分析进一步提取信号特征,构建并利用多层梯度下降法训练多个深度神经网络,将所提取的特征输入至对应深度神经网络中,经由Softmax函数输出混合式入侵行为检测结果,最后采用改进的DS证据理论关联融合各深度神经网络输出的检测结果,实现光纤传感网络混合式入侵行为实时检测。实验结果表明,所提方法入侵行为检测结果更准确、内存占用率和CPU使用率较低。Hybrid intrusion behavior often only occurs in one or more local locations,and there is a certain degree of aggregation in time,which cannot capture its complex features well.Therefore,a real-time detection method for hybrid intrusion behavior in fiber optic sensing networks is proposed.Using the average zero crossing rate and short-term energy as indicators to segment a certain segment of the signal,reducing the accumulated processing delay,and extracting fiber optic sensing signals that may have intrusion behavior.Further extracting signal features through high-order spectral analysis,sample entropy analysis,and singular value analysis,constructing and utilizing multi-layer gradient descent method to train multiple deep neural networks,and inputting the extracted features into the corresponding deep neural networks,and the mixed intrusion behavior detection results are output through the Softmax function.Finally,the improved D-S evidence theory is used to correlate and fuse the detection results output by each deep neural network,implement real-time detection of hybrid intrusion behavior in fiber optic sensing networks.The experimental results show that the proposed method has more accurate intrusion behavior detection results,lower memory and CPU usage.

关 键 词：光纤传感网络 混合式入侵行为 实时检测 深度神经网络 奇异值分解 

分 类 号：TN931[电子电信—信号与信息处理]