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    • 基于机器学习的光网络干扰攻击检测、识别与恢复方法

    GONG Xiaoxue, PANG Jiahao,ZHANG Qihan, XU Changle, QIN Wenshuai,GUO Lei

    Journal on Communications（2023）

    重庆邮电大学通信与信息工程学院

    Cited 0|Views5
    Abstract
    光网络由于其结构的脆弱性,容易受到旨在中断通信服务的信号干扰攻击.基于此,提出了一种基于机器学习的攻击检测、识别与恢复框架.在攻击检测与识别方面,评估了 BiLSTM、1DCNN 和 7 种常规机器学习分类器(ANN、DT、KNN、LDA、NB、RF和SVM)在检测攻击是否存在,以及识别受到的不同类型的干扰攻击上的性能.在攻击恢复方面,提出了基于 BiLSTM-BiGRU 的干扰攻击恢复模型,分别用来恢复轻度带内、强度带内、轻度带外和强度带外干扰攻击.数值仿真结果表明,所提模型表现出优异的性能,检测与识别准确率高达99.20%,针对4种攻击的恢复率分别为95.05%、97.03%、94.06%和61.88%.
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    Key words
    machine learning,attack detection and identification,attack recovery,optical network security
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    • Pretraining has recently greatly promoted the development of natural language processing (NLP)
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