Generative Adversarial Imputation With Relation‐Aware Self‐Attention for Robust Data Recovery

Owing to the highly networked and cyber‐enabled nature of cyber‐physical systems (CPSs), they are particularly susceptible to various cyber‐attacks and communication anomalies. Such vulnerabilities can lead to critical data loss, which in turn impairs subsequent tasks such as state estimation. Moreover, the above issues may render the prior information of the physical topology unreliable or unavailable in real time. To address these challenges, an improved data‐driven generative adversarial imputation network (GAIN) is proposed to recover CPS data subjected to denial of service (DoS) attacks or missing‐data scenarios. Building upon the original GAIN framework, we improve the generator by incorporating a RASA mechanism, which explicitly models both explicit and implicit token relationships to capture complex data interdependencies. Furthermore, this mechanism enhances the traceability and interpretability of attention allocation, making the proposed approach particularly well‐suited for time‐series data processing. Furthermore, the joint optimization training framework is introduced, through which the synergistic effect of the masked imputation task (MIT) and the observed reconstruction task (ORT), combined with the parallel processing advantage of the self‐attention mechanism, collectively enhances the model's imputation performance, robustness and generalization capability, thereby further strengthening the data recovery capacity of the proposed GAIN with RASA (RASA‐GAIN). Experimental results on multiple datasets, including secure water treatment (SWaT), demonstrate that the proposed RASA‐GAIN achieves an average reduction in RMSE of 53.45% on Dataset A of SWaT, 8.59% on the Letter dataset and 23.25% on the News dataset, compared to the original GAIN. Furthermore, to assess its proficiency in time‐series data recovery, we deploy the proposed RASA‐GAIN to mitigate missing‐data challenges in power systems facing both DoS attacks and random packet losses. The results indicate that RASA‐GAIN outperforms five mainstream imputation models in most scenarios, delivering superior data recovery performance across missing rates ranging from 14.594% to 57.229%.