用机器学习提升工控物联网电表网络的入侵检测能力
Machine Learning-Based Intrusion Detection and Prevention System for IIoT Smart Metering Networks: Challenges and Solutions
- 结合签名与异常检测，构建机器学习驱动的入侵防护系统
- 实验证明该系统能有效识别多种网络攻击，提升安全韧性
- 适合关注工业物联网安全的工程师和研究人员
工业互联网（IIoT）通过实现自动化、实时数据交换和智能决策，推动了产业变革。然而，其更高的连接性也带来了网络安全威胁，尤其是在监控与优化能源消耗的关键智能电表网络中。本文探讨了基于IIoT的智能电表网络面临的安全挑战，并提出一种基于机器学习（ML）的入侵检测与防护系统（IDPS），以保护边缘设备。研究回顾了多种入侵检测方法，分析了基于签名与基于异常检测技术的优势与局限。结果表明，在IIoT智能电表环境中集成机器学习驱动的IDPS，可显著增强安全性、效率与对持续演变的网络攻击的抵御能力。
原文摘要 · Abstract (English)
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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