摘要 无人机自组织网络是移动自组织网络的一种,节点的不间断移动可以使其随意的加入或者离开网络,使得混入其中的恶意节点可以针对网络信息进行窃取、篡改乃至攻击摧毁整个网络.开放的体系结构和动态的拓扑使得无人机网络容易受到来自各个网络层次的攻击.本文针对无人机自组网中可能存在的攻击行为以及恶意节点检测的问题,提出了一种起源信息感知的无人机网络信任模型(A provenance-aware trust model for Unmanned Aerial Vehicle Networks,UAVNpro),旨在实现准确的对等信任评估,在最大化报文交付率的同时减少资源受限的网络环境下的消息时延和通信成本.起源信息是指网络中传输报文的所有历史,基于报文的完整性可以对报文创建和操作节点的行为做出有效评判并生成观测证据.通过收集证据进行信任评估就可以识别网络中的恶意节点并进行路由隔离.UAVNpro采用数据驱动的方法减少识别恶意节点时的资源损耗,同时利用数字签名技术保证数据的安全传输.经过实验分析表明,UAVNpro与现有的无人机网络路由协议有良好的兼容性,可针对无人机网络中的丢包、注包、信息篡改、假身份等攻击行为做出有效的识别.UAVNpro在恶意节点检测率、报文的投递率和系统能耗上都要优于现有模型.
Abstract：Unmanned Aerial Vehicle Network is one of an ad-hoc network.The mobile nodes are free to join or leave the network at will,making it easy for malicious nodes to sneak into the network.Malicious nodes not only steal and tamper messages but also attack and destroy the entire network.The open architecture and dynamic topology make Unmanned Aerial Vehicle Networks vulnerable to a variety of attacks at all layers.This work proposes a provenance-aware trust model for Unmanned Aerial Vehicle Networks,namely UAVNpro that aims to achieve accurate peer-to-peer trust assessment and maximize the delivery of correct messages received by destination nodes while minimizing message delay and communication cost under resource-constrained network environments.Provenance refers to the history of ownership of messages transmitted on the network.The behavior of messages creators and operators can be effectively evaluated based on message integrity,then generate the observational evidence.We can collect the observational evidence for trust evaluation,then identify malicious nodes in the network and isolate them from the network.UAVNpro takes a data-driven approach to reduce resource consumption in the presence of selfish or malicious nodes while ensuring the safe transmission of data by digital signature technology.The experiment shows that UAVNpro compatibly well with the existing UAV network routing protocols,and can effectively identify attacks such as black hole,grey hole,message modification,fake recommendation and fake identity in UAV networks.UAVNpro is superior to the existing security model in terms of detection rate,delivery rate and system energy consumption in most cases.
2020, Vol. 41 
