基于生成对抗网络的UWSNs恶意节点检测方法  

Malicious Nodes Detection Method for UWSNs Based on Generative Adversarial Network

作　　者：杨柳 王宇[1] 陶洋[1] YANG Liu;WANG Yu;TAO Yang(School of Communications and Information Engineering,Chongqing University of Posts and Telecommunications,Chongqing 400065,China)

机构地区：[1]重庆邮电大学通信与信息工程学院,重庆400065

出　　处：《通信技术》2024年第5期512-518,共7页Communications Technology

基　　金：中国博士后科学基金(2023MD744139);重庆市教育委员会科学技术研究项目(KJQN202300653)。

摘　　要：水下无线传感器网络(Underwater Wireless Sensor Networks,UWSNs)节点通常部署于无人值守的恶劣环境中,恶意节点的存在对网络的安全构成了极大威胁。为了在缺乏节点行为证据的情况下有效检测恶意节点,提出了一种基于生成对抗网络(Generative Adversarial Network,GAN)的恶意节点检测方法。首先,构建了基于GAN的异常预测模型;其次,收集数据、链路、能量等多维信任证据,构建出训练数据集;最后,训练异常预测模型,并通过信任决策检测恶意节点。仿真结果表明,该方法可以在不需要大量信任证据的情况下有效地进行恶意节点检测,并且与同类型方法相比,所提方法具有较高的恶意节点检测率、通信成功率及较低的假阳率。UWSNs(Underwater Wireless Sensor Networks)nodes are usually deployed in unattended or harsh environments,and the presence of malicious nodes poses a great threat to the security of the networks.To effectively detect malicious nodes in the absence of evidence about node behavior,a malicious node detection method based on GAN(Generative Adversarial Network)is proposed.First,a GAN-based anomaly prediction model is constructed.Then,multi-dimensional trust evidences regarding data,link,and energy are collected to construct training dataset.Finally,the anomaly prediction model is trained and malicious nodes are detected through trust decision.Simulation results indicate that the method can effectively perform malicious node detection without large numbers of trust evidences.In addition,the proposed method has a better performance on malicious node detection rate,communication success rate,and low false positive rate than other state-of-the-art methods.

关 键 词：水下无线传感器网络 生成对抗网络 异常预测 信任证据 信任决策 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]