  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.
