【Author】
Liu, Hong; Zhang, Shuaipeng; Zhang, Pengfei; Zhou, Xinqiang; Shao, Xuebin; Pu, Geguang; Zhang, Yan
【Source】IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
【Abstract】The vehicular networks constructed by interconnected vehicles and transportation infrastructure are vulnerable to cyber-intrusions due to the expanded use of software and the introduction of wireless interfaces. Intrusion detection systems (IDSs) can be customized efficiently in response to this increased attack surface. There has been significant progress in detecting malicious attack traffic using machine learning approaches. However, existing IDSs require network devices with powerful computing capabilities to continuously train and update complex network models, which reduces the efficiency and defense capability of intrusion detection systems due to limited resources and untimely model updates. This work proposes a cooperative intrusion detection mechanism that offloads the training model to distributed edge devices (e.g., connected vehicles and roadside units (RSUs). Distributed federated-based approach reduces resource utilization of the central server while assuring security and privacy. To ensure the security of the aggregation model, blockchain is used for the storage and sharing of the training models. This work analyzes common attacks and shows that the proposed scheme achieves cooperative privacy-preservation for vehicles while reducing communication overhead and computation cost.
【Keywords】Blockchain; Training; Intrusion detection; Data models; Collaborative work; Machine learning; Image edge detection; Intrusion detection; federated learning; vehicular networks; blockchain
【标题】区块链和联邦学习在车辆边缘计算中的协同入侵检测
【摘要】由于软件的扩展使用和无线接口的引入，由互联车辆和交通基础设施构建的车辆网络很容易受到网络入侵。入侵检测系统 (IDS) 可以针对这种增加的攻击面进行有效定制。在使用机器学习方法检测恶意攻击流量方面取得了重大进展。然而，现有的入侵检测系统需要具备强大计算能力的网络设备来不断地训练和更新复杂的网络模型，由于资源有限和模型更新不及时，从而降低了入侵检测系统的效率和防御能力。这项工作提出了一种协作入侵检测机制，将训练模型卸载到分布式边缘设备（例如，连接的车辆和路边单元（RSU）。基于分布式联邦的方法在确保安全性和隐私性的同时降低了中央服务器的资源利用率。为了确保在聚合模型的安全性方面，区块链用于训练模型的存储和共享，本文分析了常见的攻击，表明该方案在降低通信开销和计算成本的同时，实现了车辆的协同隐私保护。
【关键词】区块链；训练;入侵检测;数据模型；协作工作；机器学习；图像边缘检测；入侵检测;联邦学习；车载网络；区块链
评论