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基于变分自动编码器的水下传感器网络信任模型

张家豪 魏思周 夏娜

张家豪, 魏思周, 夏娜. 基于变分自动编码器的水下传感器网络信任模型[J]. 水下无人系统学报, 2025, 33(2): 1-10 doi: 10.11993/j.issn.2096-3920.2024-0181
引用本文: 张家豪, 魏思周, 夏娜. 基于变分自动编码器的水下传感器网络信任模型[J]. 水下无人系统学报, 2025, 33(2): 1-10 doi: 10.11993/j.issn.2096-3920.2024-0181
ZHANG Jiahao, WEI Sizhou, XIA Na. The trust model of underwater sensor networks based on variational autoencoders[J]. Journal of Unmanned Undersea Systems. doi: 10.11993/j.issn.2096-3920.2024-0181
Citation: ZHANG Jiahao, WEI Sizhou, XIA Na. The trust model of underwater sensor networks based on variational autoencoders[J]. Journal of Unmanned Undersea Systems. doi: 10.11993/j.issn.2096-3920.2024-0181

基于变分自动编码器的水下传感器网络信任模型

doi: 10.11993/j.issn.2096-3920.2024-0181
基金项目: 国家自然科学基金项目(61971178); 内蒙古鄂尔多斯市科技重大“揭榜挂帅”项目(JBGS-2023-002); 国家电网科技项目(5500-202440171A-1-1-ZN); 国家电网科技项目(5500-202140127A).
详细信息
    作者简介:

    张家豪(2002-), 男, 在读硕士, 主要研究方向为水下传感器网络、机器学习

    通讯作者:

    魏思周(1997-), 男, 在读博士, 主要研究方向为水下传感器网络、人工智能及网络安全.

  • 中图分类号: TN929; TP3

The trust model of underwater sensor networks based on variational autoencoders

  • 摘要: 在水下传感器网络中, 复杂的水声通信环境与受限的节点资源使恶意节点的攻击更具隐蔽性和威胁性, 因此研究有效的恶意节点检测方法对维护网络稳定与数据安全至关重要。文中提出了一种基于变分自动编码器的水下传感器网络信任模型, 通过评估节点行为可信度来判定是否为恶意节点。首先汇聚节点收集水下节点传输过程中的行为特征数据, 从中提取位置、数据包投递比率及延迟等指标, 构成信任数据集; 然后对数据集进行编码训练, 利用变分推理将数据映射到潜在空间, 并获得该空间的概率分布; 最后依据概率分布解码重构数据, 得到节点行为可信度, 从而完成对节点的信任评估。对比实验结果表明, 相较于Itrust等方法, 该模型在信任评估准确度方面至少提升了10.5%, 同时在运行稳定性方面具有明显的优势。

     

  • 图  1  UWSNs中的安全图

    Figure  1.  Security diagram in UWSNs

    图  2  VAE结构图

    Figure  2.  Structure of the VAE

    图  3  VAE信任评估模型框架图

    Figure  3.  Framework diagram of the VAE trust assessment model

    图  4  VAE算法伪代码

    Figure  4.  Pseudocode of VAE algorithm

    图  5  每个轮次期间的损失

    Figure  5.  Loss during each epoch

    图  6  各模型性能随恶意节点的比例变化情况

    Figure  6.  The performance of each model under varying malicious node ratios

    图  7  各模型性能在特定攻击下随恶意节点比例变化的表现情况

    Figure  7.  The performance of each model under specific attacks varying maliciousnode ratios

    图  8  各模型性能随时间段变化情况

    Figure  8.  The performance of each model varies over time periods

    图  9  各模型性能在特定攻击下随时间段变化情况

    Figure  9.  The performance of each model under specific attacks over different time intervals

    表  1  VAE的参数列表

    Table  1.   Parameter list of VAE

    参数
    监控区域/m3500×500×500
    节点数量200
    初始能量/J1 000
    特征数量5
    神经元数量6 464
    潜在空间纬度2
    隐藏层激活函数ReLU
    输出层激活函数Sigmoid
    优化器Adam
    迭代次数300
    下载: 导出CSV

    表  2  VAE信任模型检测性能结果

    Table  2.   Detection performance results of the vae trust model

    节点
    个数
    恶意节
    点比例
    AUC 真阴性
    (TN)
    真阳性
    (TP)
    假阴性
    (FN)
    假阳性
    (FP)
    50 0.1 0.94 43±1 4±1 1±1 1±1
    0.2 0.92 38±2 8±1 2±2 2±1
    0.3 0.89 33±2 13±1 2±2 2±1
    100 0.1 0.94 89±1 9±1 1±1 1±1
    0.2 0.93 78±2 18±2 2±2 2±2
    0.3 0.89 65±3 26±2 4±3 3±2
    200 0.1 0.94 176±3 16±2 3±3 3±2
    0.2 0.90 154±4 34±3 5±4 5±3
    0.3 0.88 131±3 52±3 7±3 7±3
    下载: 导出CSV
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  • 被引次数: 0
出版历程
  • 收稿日期:  2024-12-30
  • 修回日期:  2025-02-17
  • 录用日期:  2025-02-25
  • 网络出版日期:  2025-03-11

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