Securing AMI-IoT networks against multiple RPL attacks using ensemble learning IDS and light-chain based prediction detection and mitigation mechanisms

ABSTRACT Advanced Metering Infrastructure (AMI) is one of the Internet of Things (IoT) enabled smart applications of smart grids. The Routing Protocol for Low Power and Lossy network (RPL) has been accepted to facilitate effective routing services for the AMI. However, numerous RPL attacks appear in AMI due to resource scarcity and dubious wireless medium, which significantly impedes the successful deployment of AMI-RPL. To enable secure and reliable AMI-RPL, this work proposes a novel Intrusion Detection System (IDS) named AMI Attack-aware Intelligent Machine learning IDS (AIMS). The primary objectives of AIMS are to predict, detect, and mitigate different types of RPL security attacks in the AMI environment. To predict the RPL attacks using the Stacked Ensemble (SE) machine learning model, a novel AMI-RPL Attack Dataset (ARAD) is generated by the Cooja simulator with the suitable pre-processing and the Spider Monkey Optimization (SMO) based feature selection. The advanced prediction of attack nodes improves the performance and significantly diminishes the future damages of AMI. The attack detection is based on immutable blocks of a light-chain model, and the cryptocurrency-based mitigation model effectively isolates the attackers. AIMS mechanism amplifies RPL security with high reliability and maximizes the AMI network lifetime by delivering superior results.