摘要
多模光纤网络由于模式色散大,使其带宽较低,在多用户间连通使用易受到异常信号入侵攻击,导致其安全性较差。为提高异常入侵信号的检测准确率,增强光纤网络安全性,提出一种经验模态分解方法结合深度学习的多模光纤网络异常入侵信号提纯方法。针对光纤网络容易发生流量拥塞的问题,进行异常流量,提高网络安全性,提出基于深度学习的光纤网络异常流量检测方法。构建光纤网络的信号传输结构模型,采用非线性时间序列重构方法进行光纤网络异常入侵信号的特征提取,检测异常信号的时频谱特征量,结合经验模态分解方法实现光纤网络异常入侵信号特征信息分离,采用深度学习算法进行异常入侵信号特征分解的自适应迭代,提高异常入侵信号检测的收敛性,将特征分离后的入侵信号输入到降噪滤波器中,进行信号提纯处理,完成网络异常入侵信号检测。仿真结果表明,采用该方法进行光纤网络异常入侵信号检测的抗干扰能力较强,准确检测概率较高,收敛性较好,从而提高了检测效率,改善了多模光纤网络的安全性。
Because of the large dispersion of the mode,the bandwidth of the multimode fiber network is low,and it is vulnerable to the intrusion attack of the abnormal signal in the connection between the multi-users,which leads to the poor security of the network. In order to improve the detection accuracy of abnormal intrusion signals and enhance the security of optical fiber networks,an empirical mode decomposition method combined with depth learning is proposed to purify abnormal intrusion signals from multi-mode optical networks. Aiming at the problem that the fiber network is prone to traffic congestion,this paper proposes an anomaly traffic detection method based on depth learning to improve the network security. The signal transmission structure model of fiber optic network is constructed,and the nonlinear time series reconstruction method is adopted. The feature extraction of abnormal intrusion signal in optical fiber network,the detection of time spectrum characteristic quantity of abnormal signal,and the separation of characteristic information of abnormal intrusion signal of optical fiber network with empirical mode decomposition method are realized. The adaptive iteration of feature decomposition of abnormal intrusion signal is carried out by using depth learning algorithm to improve the convergence of abnormal intrusion signal detection. The intrusion signal after feature separation is input into the denoising filter for signal purification. Complete network anomaly intrusion signal detection. The simulation results show that the proposed method has strong anti-interference ability,higher detection probability and better convergence. The measurement efficiency improves the security of multimode fiber network.
引文
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