Machine Learning-Based Intrusion Detection and Prevention System for IIoT Smart Metering Networks: Challenges and Solutions

Abstract: The Industrial Internet of Things (IIoT) has revolutionized industries by enabling automation, real-time data exchange, and smart decision-making. However, its increased connectivity introduces cybersecurity threats, particularly in smart metering networks, which play a crucial role in monitoring and optimizing energy consumption. This paper explores the challenges associated with securing IIoTbased smart metering networks and proposes a Machine Learning (ML)-based Intrusion Detection and Prevention System (IDPS) for safeguarding edge devices. The findings suggest that integrating ML-driven IDPS in IIoT smart metering environments enhances security, efficiency, and resilience against evolving cyber threats. Keywords: IIoT, Smart Metering, Intrusion Detection System (IDS), Intrusion Prevention System (IPS), Machine Learning, Cybersecurity, Anomaly Detection, Edge Computing, Network Security, Smart Grid. 1. Introduction Everything globally, from body sensors to contemporary cloud computing, is included in the Internet of Things (IoT). It creates a sophisticated distributed system by connecting humans, machines, and networks everywhere; it improves the quality of human life by enabling reliable machine-to-machine and machineto-human connections [1]. The integration of conventional Internet of Things (IoT) principles in manufacturing industries and applications is referred to as the Industrial Internet of Things (IIoT) [2].