用于检测网络攻击并区分电力系统扰动类型的机器学习方法
Machine Learning to detect cyber-attacks and discriminating the types of power system disturbances
AI总结：
本文提出基于机器学习的智能电网攻击检测模型，利用PMU数据与日志，通过随机森林、逻辑回归和K近邻三种模型对比，其中随机森林以90.56%准确率最优，可辅助操作员决策。
摘要：
本研究提出了一种基于机器学习的电力系统攻击检测模型，特别针对智能电网。通过利用从相量测量装置（PMU）收集的数据和日志，该模型旨在学习系统行为并有效识别潜在的安全边界。所提出的方法包括数据集预处理、特征选择、模型构建和评估等关键阶段。为验证我们的方法，我们使用了一个由来自不同PMU、继电器Snort警报和日志的15个独立数据集组成的数据集。构建了三种机器学习模型：随机森林、逻辑回归和K近邻，并使用各种性能指标进行评估。研究结果表明，随机森林模型在检测电力系统扰动方面实现了最高性能，准确率达到90.56%，并具有辅助操作员进行决策过程的潜力。
英文摘要：
This research proposes a machine learning-based attack detection model for power systems, specifically targeting smart grids. By utilizing data and logs collected from Phasor Measuring Devices (PMUs), the model aims to learn system behaviors and effectively identify potential security boundaries. The proposed approach involves crucial stages including dataset pre-processing, feature selection, model creation, and evaluation. To validate our approach, we used a dataset used, consist of 15 separate datasets obtained from different PMUs, relay snort alarms and logs. Three machine learning models: Random Forest, Logistic Regression, and K-Nearest Neighbour were built and evaluated using various performance metrics. The findings indicate that the Random Forest model achieves the highest performance with an accuracy of 90.56% in detecting power system disturbances and has the potential in assisting operators in decision-making processes.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
