Machine Learning-Based Intrusion Detection and Prevention System for IIoT Smart Metering Networks: Challenges and Solutions
This addresses security challenges for IIoT smart metering networks, but it appears incremental as it reviews existing approaches and proposes an integration without new benchmarks or specific gains.
The paper tackles cybersecurity threats in Industrial Internet of Things (IIoT) smart metering networks by proposing a Machine Learning-based Intrusion Detection and Prevention System (IDPS), finding that it enhances security, efficiency, and resilience against evolving threats.
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.