Deep learning methods can not only detect false data injection attacks (FDIA) but also locate attacks of FDIA. Although adversarial false data injection attacks (AFDIA) based on deep learning vulnerabilities have been studied in the field of single-label FDIA detection, the adversarial attack and defense against multi-label FDIA locational detection are still not involved. To bridge this gap, this paper first explores the multi-label adversarial example attacks against multi-label FDIA locational detectors and proposes a general multi-label adversarial attack framework, namely muLti-labEl adverSarial falSe data injectiON attack (LESSON). The proposed LESSON attack framework includes three key designs, namely Perturbing State Variables, Tailored Loss Function Design, and Change of Variables, which can help find suitable multi-label adversarial perturbations within the physical constraints to circumvent both Bad Data Detection (BDD) and Neural Attack Location (NAL). Four typical LESSON attacks based on the proposed framework and two dimensions of attack objectives are examined, and the experimental results demonstrate the effectiveness of the proposed attack framework, posing serious and pressing security concerns in smart grids.

翻译：深度学习方法不仅能检测虚假数据注入攻击（FDIA），还能定位攻击位置。尽管基于深度学习漏洞的对抗性虚假数据注入攻击（AFDIA）在单标签FDIA检测领域已有研究，但针对多标签FDIA定位检测的对抗攻击与防御仍未被涉及。为填补这一空白，本文首次探索了针对多标签FDIA定位检测器的多标签对抗性示例攻击，并提出通用多标签对抗攻击框架——LESSON（muLti-labEl adverSarial falSe data injectiON attack）。该LESSON攻击框架包含三个关键设计：状态变量扰动、定制化损失函数设计、变量变换，可帮助在物理约束内寻找合适的多标签对抗性扰动，从而规避不良数据检测（BDD）与神经攻击定位（NAL）。基于所提框架，从攻击目标的两个维度考察了四种典型LESSON攻击。实验结果表明，该攻击框架具有有效性，对智能电网构成严峻且紧迫的安全威胁。