用机器学习实时识别电网攻击、自然事件和无事件，提升安全监测能力。
Cybersecurity Assessment of Smart Grid Exposure Using a Machine Learning Based Approach
- 基于XGB分类器构建模型，区分电网三类事件。
- 在三个子数据集上各项指标表现良好，分类准确率高。
- 适合电力系统安全监控与威胁检测人员参考。
随着电力系统运行受干扰现象急剧增加，尤其是未经授权访问敏感关键数据、恶意软件注入以及因补丁缺失导致的安全漏洞利用等问题日益严重，开发具备机器学习能力的实时评估系统以应对快速演变的网络攻击，不仅对保障电力系统的安全、可靠和稳定运行至关重要，也关乎先进监控与高效威胁检测的实现。研究采用密西西比州立大学与橡树岭国家实验室的数据集，使用XGB分类器模型对电力系统扰动进行诊断与评估，涵盖攻击事件、自然事件和无事件三类。测试结果表明，该模型在所有三个子数据集上均表现出良好的性能，能够准确识别并分类各类电力系统事件。
原文摘要 · Abstract (English)
Given that disturbances to the stable and normal operation of power systems have grown phenomenally, particularly in terms of unauthorized access to confidential and critical data, injection of malicious software, and exploitation of security vulnerabilities in a poorly patched software among others; then developing, as a countermeasure, an assessment solutions with machine learning capabilities to match up in real-time, with the growth and fast pace of these cyber-attacks, is not only critical to the security, reliability and safe operation of power system, but also germane to guaranteeing advanced monitoring and efficient threat detection. Using the Mississippi State University and Oak Ridge National Laboratory dataset, the study used an XGB Classifier modeling approach in machine learning to diagnose and assess power system disturbances, in terms of Attack Events, Natural Events and No-Events. As test results show, the model, in all the three sub-datasets, generally demonstrates good performance on all metrics, as it relates to accurately identifying and classifying all the three power system events.
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