The IPv6 routing protocol for low power and lossy networks (RPL) has gained widespread application in the Internet of Things (IoT) environment. RPL has inherent security features to restrict external attacks. However, internal attacks in the IoT environment have continued to grow due to the lack of mechanisms to manage the secure identities and credentials of the billions of heterogeneous IoT devices. Weak credentials aid attackers in gaining access to IoT devices and further exploiting vulnerabilities stemming from the underlying routing protocols. Routing attacks degrade the performance of IoT networks by compromising the network resources, topology, and traffic. In this paper, we propose a holistic framework for the prediction of routing attacks in RPL-based IoT. The framework leverages Graph Convolution Network-based network embedding to capture and learn the latent state of the nodes in the IoT network. It uses a Long Short Term Memory model to predict network traffic. The framework incorporates a Feedforward Neural Network that uses network embedding and traffic prediction as input to predict routing attacks. The accuracy of any learning model depends on the integrity of the data provided to it as input. Therefore, the framework uses smart contract-fortified blockchain technology to establish secure channels for IoT data access. The smart contract within the blockchain generates warning impulses in the case of abnormal behavior of nodes. The framework predicts normal scenarios, resource attack scenarios, traffic attack scenarios, and topological attack scenarios with a fair accuracy of 94.5%, 82.46%, 91.88%, and 86.13%, respectively.

用于低功耗和有损网络 (RPL) 的 IPv6 路由协议已在物联网 (IoT) 环境中获得广泛应用。RPL 具有固有的安全特性来限制外部攻击。然而，由于缺乏管理数十亿异构物联网设备的安全身份和凭证的机制，物联网环境中的内部攻击持续增长。弱凭据可帮助攻击者访问 IoT 设备并进一步利用源自底层路由协议的漏洞。路由攻击通过危及网络资源、拓扑和流量来降低物联网网络的性能。在本文中，我们提出了一个整体框架来预测基于 RPL 的物联网中的路由攻击。该框架利用基于图卷积网络的网络嵌入来捕获和学习物联网网络中节点的潜在状态。它使用长期短期记忆模型来预测网络流量。该框架包含一个前馈神经网络，该网络使用网络嵌入和流量预测作为输入来预测路由攻击。任何学习模型的准确性都取决于作为输入提供给它的数据的完整性。因此，该框架使用智能合约强化的区块链技术来建立物联网数据访问的安全通道。区块链内的智能合约在节点出现异常行为时产生预警脉冲。该框架预测正常场景、资源攻击场景、流量攻击场景和拓扑攻击场景，准确率分别为94.5%、82.46%、