基于强化学习的物联网安全资源分配方法  

Security Resource Allocation Method for Internet of Things Based on Reinforcement Learning

作　　者：赵洪 李姗 左珮良 魏占祯[2] ZHAO Hong;LI Shan;ZUO Peiliang;WEI Zhanzhen(University of Science and Technology of China,Hefei 230026,China;Beijing Electronic Science and Technology Institute,Beijing 100070,China)

机构地区：[1]中国科学技术大学,合肥230026 [2]北京电子科技学院,北京100070

出　　处：《信息网络安全》2022年第6期44-52,共9页Netinfo Security

基　　金：国家自然科学基金[62001251,62001252];国家重点研发计划[2018YFE0200600];北京高校高精尖学科建设项目[202100130401];西安电子科技大学综合业务网理论及关键技术国家重点实验室开放课题[ISN22-13]。

摘　　要：雾计算作为一种分散式计算结构应用于物联网中,其固有的广播特性导致网络通信系统面临严重的安全威胁。同时,在动态变化的物联网环境中,无线资源的合理分配对减少通信服务时延至关重要。文章在存在不可信节点的前提下,对物联网安全资源分配问题进行研究,基于雾节点接收机具备同时同频全双工自干扰消除技术的假定,提出一种具有物理层安全特性的针对雾层无线资源的智能分配方法。该方法通过构建深度强化学习神经网络,设计合理的状态、动作和奖励等参数,实现物联网感知数据在安全保密防护条件下的快速上传。实验结果表明,该方法收敛速度较快,且在性能上明显优于对比方法。As a decentralized computing structure,fog computing is applied in the Internet of things,and its inherent broadcast characteristics will make the network communication system to confront serious security threats.At the same time,in the dynamically changing fog Internet of things environment,the rational allocation of wireless resources is crucial to reduce the delay of communication services.Under the premise of the existence of untrusted nodes,this paper studies the security resource allocation problem of fog Internet of things.Based on the assumption that fog node receivers have simultaneous co-frequency full-duplex self-interference cancellation technology,a new method with physical layer security characteristics is proposed.It is an intelligent allocation method for radio resources in the fog layer.By constructing a deep reinforcement learning neural network and designing reasonable parameters such as state,action and reward,the method realizes the rapid upload of fog IoT perception data under the condition of security and confidentiality protection.The experimental results show that the method has a faster convergence speed and is significantly better than the comparison methods in performance.

关 键 词：物联网 强化学习 资源分配 物理层安全 保密通信 

分 类 号：TP309[自动化与计算机技术—计算机系统结构]