Large Language Models for Detecting Cyberattacks on Smart Grid Protective Relays

This paper presents a large language model (LLM)-based framework for detecting cyberattacks on transformer current differential relays (TCDRs), which, if undetected, may trigger false tripping of critical transformers. The proposed approach adapts and fine-tunes compact LLMs such as DistilBERT to distinguish cyberattacks from actual faults using textualized multidimensional TCDR current measurements recorded before and after tripping. Our results demonstrate that DistilBERT detects 97.6% of cyberattacks without compromising TCDR dependability and achieves inference latency below 6 ms on a commercial workstation. Additional evaluations confirm the framework's robustness under combined time-synchronization and false-data-injection attacks, resilience to measurement noise, and stability across prompt formulation variants. Furthermore, GPT-2 and DistilBERT+LoRA achieve comparable performance, highlighting the potential of LLMs for enhancing smart grid cybersecurity. We provide the full dataset used in this study for reproducibility.

RadGrad: Active learning with loss gradients

Solving sequential decision prediction problems, including those in imitation learning settings, requires mitigating the problem of covariate shift. The standard approach, DAgger, relies on capturing expert behaviour in all states that the agent reaches. In real-world settings, querying an expert is costly. We propose a new active learning algorithm that selectively queries the expert, based on both a prediction of agent error and a proxy for agent risk, that maintains the performance of unrestrained expert querying systems while substantially reducing the number of expert queries made. We show that our approach, RadGrad, has the potential to improve upon existing safety-aware algorithms, and matches or exceeds the performance of DAgger and variants (i.e., SafeDAgger) in one simulated environment. However, we also find that a more complex environment poses challenges not only to our proposed method, but also to existing safety-aware algorithms, which do not match the performance of DAgger in our experiments.