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

arXiv: https://arxiv.org/abs/2502.11138 · HTML: https://mlsanity.com/paper/2502.11138/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 56 references that are in the map.
- First posted on arXiv 16 Feb 2025.

## Builds on (most cited first, 3 of 3)

- [Anomaly-based Framework for Detecting Power Overloading Cyberattacks in Smart Grid AMI](https://mlsanity.com/paper/2407.03264/) (2024)
- [Multiclass Classification Procedure for Detecting Attacks on MQTT-IoT Protocol](https://mlsanity.com/paper/2402.03270/) (2024)
- [An Efficient Security Model for Industrial Internet of Things (IIoT) System Based on Machine Learning Principles](https://mlsanity.com/paper/2502.06502/) (2025)

## Abstract (arXiv)

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.