Defense Methods for Adversarial Attacks Against Power CPS Data-Driven Algorithms

doi:10.11930/j.issn.1004-9649.202312067

Abstract

Abstract:

The integration of large-scale power electronic devices has introduced a large number of strong nonlinear measurement/control nodes into the system, gradually transforming the traditional power system into a cyber physical system (CPS). Many system problems that were originally solved by model-driven methods have had to be analyzed using data-driven algorithms due to limitations such as dimensional disasters. However, the inherent flaws of data-driven algorithms introduce new risks to the safe and stable operation of the system, which attackers can exploit to launch adversarial attacks that may cause system power outages and even instability. In response to the potential adversarial attacks on data-driven algorithms in power CPS, this paper proposes corresponding defense methods from such three aspects as abnormal data filtering and recovery, algorithm vulnerability mining and optimization, and algorithm self interpretability improvement: abnormal data filter, GAN-based vulnerability mining and optimization method, data knowledge fusion model and its training method. The effectiveness of the proposed method is verified through case analysis.

Key words: adversarial attacks, data-driven algorithms, power CPS, attack defense

Weiping ZHU, Yi TANG, Xingshen WEI, Zengji LIU. Defense Methods for Adversarial Attacks Against Power CPS Data-Driven Algorithms[J]. Electric Power, 2024, 57(9): 32-43.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202312067

https://www.electricpower.com.cn/EN/Y2024/V57/I9/32

Figures/Tables 11

Fig.1 Structure of input data filtering scheme

Fig.2 The GAN-based vulnerability mining and optimization method

Fig.3 Architecture of the data knowledge fusion model

Fig.4 Structure of training method for the GAN-based data knowledge fusion model

Fig.5 Topology structure of a 39-node system with distributed power sources

Fig.6 Comparison of indicators between normal data and abnormal sample data

Fig.7 Attack sample detection results

Table 1 Comparison of output accuracy of data-driven algorithms 单位：%

预测系统稳定数据驱动算法	不接入过滤器		接入过滤器
预测系统稳定数据驱动算法	无攻击	有攻击	无攻击	有攻击
AlexNet	98.75	56.0	97.0	92.8
VGG16	98.75	40.0	94.8	89.8
ResNet	99.25	72.0	97.5	94.2
InceptionV3	98.50	74.0	96.0	91.8

Table 2 Robustness metrics of different models under adversarial attacks

模型类型	数据驱动算法	鲁棒性指标$ \sigma $
数据驱动模型	ELM	0.4061
	AlexNet	0.3546
	SqueezeNet	0.3890
初始融合模型	IEEAC+ELM	0.3142
	IEEAC+AlexNet	0.2656
	IEEAC+SqueezeNet	0.2986
完备融合模型	IEEAC+ELM	0.1662
	IEEAC+AlexNet	0.1461
	IEEAC+SqueezeNet	0.2200

Fig.8 The impact of adversarial attacks on different models

Fig.9 The situation of attacked nodes in different models under adversarial attacks

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