Attention-aware deep reinforcement learning for detecting false data injection attacks in smart grids

•As the attack vectors injected by FDIA in power systems become more and more concealed, a model-free FDIA detection measurement method focusing on state attention is proposed.•Extracting potential features of the state vector rather than distracting from irrelevant details by introducing an attention mechanism to improve the discrimination of state observations in the MDP and to guide agents to make rational decisions.•Attention weights of each measurement point reveal the potential behavioral changes of attack points and attack strength selection when attackers construct attacks.•The proposed method is experimented on IEEE-14 and IEEE-118 bus systems and demonstrates the method’s satisfaction in terms of detection accuracy and efficiency.