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

• URL: http://arxiv.org/abs/2502.11138v1
• Date: Sun, 16 Feb 2025 14:08:59 GMT
• Title: Machine Learning-Based Intrusion Detection and Prevention System for IIoT Smart Metering Networks: Challenges and Solutions
• Authors: Sahar Lazim, Qutaiba I. Ali,
• Abstract summary: This paper explores the challenges associated with securing IIoT-based smart metering networks. It proposes a Machine Learning-based Intrusion Detection and Prevention System (IDPS) for safeguarding edge devices.
• Score: 0.0
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• 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.

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