论文标题
基于机器学习的分布式对UWAN节点的分布式身份验证，共享信息有限
Machine Learning-Based Distributed Authentication of UWAN Nodes with Limited Shared Information
论文作者
论文摘要
我们提出了一种基于水下声学通道（UWAC）的物理层特征在水下声学网络中对收到的数据包进行身份验证的技术。几个传感器a）在收到数据包的UWAC的局部估计特征（例如，水龙头的数量或延迟传播的数量），b）通过神经网络（NN）获得压缩特征表示形式，将其表示形式传输到中心汇节点，该表示，该节点使用NN，使用NN决定是否已通过合法攻击或通过合法的攻击者传输了该数据包。尽管该系统的目的是就数据包是否真实做出二进制决定，但我们表明了具有丰富的压缩功能的重要性，同时仍考虑节点之间的传输速率限制。我们考虑了所有NN一起培训的全球培训，也考虑了本地培训，每个NN都经过单独培训。对于后一种情况，使用了NN结构和损耗函数的几种替代方法进行培训。
We propose a technique to authenticate received packets in underwater acoustic networks based on the physical layer features of the underwater acoustic channel (UWAC). Several sensors a) locally estimate features (e.g., the number of taps or the delay spread) of the UWAC over which the packet is received, b) obtain a compressed feature representation through a neural network (NN), and c) transmit their representations to a central sink node that, using a NN, decides whether the packet has been transmitted by the legitimate node or by an impersonating attacker. Although the purpose of the system is to make a binary decision as to whether a packet is authentic or not, we show the importance of having a rich set of compressed features, while still taking into account transmission rate limits among the nodes. We consider both global training, where all NNs are trained together, and local training, where each NN is trained individually. For the latter scenario, several alternatives for the NN structure and loss function were used for training.
