【Author】
Abdel-Basset, Mohamed; Moustafa, Nour; Hawash, Hossam; Razzak, Imran; Sallam, Karam M.; Elkomy, Osama M.
【Source】IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
【Abstract】With the integration of the Internet of Things (IoT) in the field of transportation, the Internet of Vehicles (IoV) turned to be a vital method for designing Smart Transportation Systems (STS). STS consist of various interconnected vehicles and transportation infrastructure exposed to cyber intrusion due to the broad usage of software and the initiation of wireless interfaces. This study proposes a federated deep learning-based intrusion detection framework (FED-IDS) to efficiently detect attacks by offloading the learning process from servers to distributed vehicular edge nodes. FED-IDS introduces a context-aware transformer network to learn spatial-temporal representations of vehicular traffic flows necessary for classifying different categories of attacks. Blockchain-managed federated training is presented to enable multiple edge nodes to offer secure, distributed, and reliable training without the need for centralized authority. In the blockchain, miners confirm the distributed local updates from participating vehicles to stop unreliable updates from being deposited on the blockchain. The experiments on two public datasets (i.e., Car-Hacking, TON_IoT) demonstrated the efficiency of FED-IDS against state-of-the-art approaches. It reveals the credibility of securing networks of intelligent transportation systems against cyber-attacks.
【Keywords】Security; Blockchains; Intrusion detection; Training; Servers; Feature extraction; Deep learning; Federated learning; deep learning (DL); intrusion detection system (IDS); vehicular edge computing (VEC); blockchain
【摘要】随着物联网（IoT）在交通领域的融合，车联网（IoV）成为设计智能交通系统（STS）的重要手段。 STS 由各种互连的车辆和交通基础设施组成，由于软件的广泛使用和无线接口的启动，它们会受到网络入侵。本研究提出了一种基于联合深度学习的入侵检测框架 (FED-IDS)，通过将学习过程从服务器转移到分布式车辆边缘节点来有效检测攻击。 FED-IDS 引入了一个上下文感知转换器网络来学习对不同类别的攻击进行分类所需的车辆交通流的时空表示。提出了由区块链管理的联合训练，以使多个边缘节点能够提供安全、分布式和可靠的训练，而无需集中授权。在区块链中，矿工确认参与车辆的分布式本地更新，以阻止不可靠的更新被存入区块链。在两个公共数据集（即 Car-Hacking、TON_IoT）上的实验证明了 FED-IDS 与最先进的方法相比的效率。它揭示了保护智能交通系统网络免受网络攻击的可信度。
【关键词】安全;区块链；入侵检测;训练;服务器；特征提取;深度学习；联邦学习；深度学习（DL）；入侵检测系统（IDS）；车辆边缘计算（VEC）；区块链
评论