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

TL;DR

This paper discusses the challenges of cybersecurity in IIoT smart metering networks and proposes a machine learning-based intrusion detection and prevention system to improve security and resilience.

Contribution

It introduces a novel ML-driven IDPS tailored for IIoT smart metering networks, addressing limitations of existing detection methods.

Findings

ML-based IDPS enhances security and resilience

Integrating ML improves detection of cyber threats

The approach outperforms traditional methods in accuracy

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 IIoT-based smart metering networks and proposes a Machine Learning (ML)-based Intrusion Detection and Prevention System (IDPS) for safeguarding edge devices. The study reviews various intrusion detection approaches, highlighting the strengths and limitations of both signature-based and anomaly-based detection techniques. The findings suggest that integrating ML-driven IDPS in IIoT smart metering environments enhances security, efficiency, and resilience against evolving cyber threats.