Impact of False Data Injection Attacks on Machine Learning-Based Cascading Failure Predictions
Impact of False Data Injection Attacks on Machine Learning-Based Cascading Failure Predictions
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DOI：
10.1109/honet59747.2023.10374702
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发表时间：
2023-12
期刊：
影响因子：
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通讯作者：
Naeem Md Sami;M. Naeini
中科院分区：
文献类型：
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作者：
Naeem Md Sami;M. Naeini
机器学习（ML）和人工智能（AI）在电力系统故障分析中越来越受欢迎，因为它们能够根据历史数据识别模式和预测情况。随着现代电网的复杂性和随机性的增加，电力系统连锁故障越来越受到人们的关注。虽然ML技术已被用于预测级联故障的各个方面，但网络攻击渗透ML框架并操纵属性以隐藏级联故障或扭曲风险评估的可能性是一个紧迫的问题。本文旨在研究虚假数据注入攻击（FDIA）对级联故障的ML预测模型的影响。我们的目标是了解这些攻击如何影响ML性能，以及这些改变如何为检测此类攻击提供见解。正如预期的那样，我们的评估表明FDIA可以改变基线精度，导致级联风险的错误预测。
Machine Learning (ML) and Artificial Intelligence (AI) are garnering popularity in power system failure analysis for their ability to recognize patterns and predict situations based on historical data. The concern over cascading failures in power systems is growing, particularly with the increasing complexity and stochastic nature of modern power grids. While ML techniques have been employed to predict various aspects of cascading failures, the potential for cyber-attacks to infiltrate ML frameworks and manipulate attributes to conceal cascading failures or distort risk assessments is a pressing issue. This paper aims to investigate the impact of false data injection attacks (FDIA) on an example ML-based prediction model for cascading failures. The objective is to understand how these attacks influence ML performance and how such alterations can provide insights to detect such attacks. As anticipated, our assessment indicates that FDIA can alter baseline accuracies, resulting in erroneous predictions of cascade risk.