# Latency-Aware Deep Learning Benchmark for Real-Time Cyber-Physical Attack and Fault Classification in Inverter-Dominated Power Grids

arXiv: https://arxiv.org/abs/2605.17256 · HTML: https://mlsanity.com/paper/2605.17256/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 10 references that are in the map.
- First posted on arXiv 17 May 2026.

## Builds on (most cited first, 1 of 1)

- [Adaptive Anomaly Detection for Identifying Attacks in Cyber-Physical Systems: A Systematic Literature Review](https://mlsanity.com/paper/2411.14278/) (2024)

## Built on by (most cited first, 3 of 3)

- [A Standardized Framework for Machine Learning in Power System Protection](https://mlsanity.com/paper/2608.20181/) (2026)
- [EvEMTBench: An Open Benchmark for Machine Learning in Power System Protection](https://mlsanity.com/paper/2609.28149/) (2026)
- [One-Cycle Fault Classification and Faulted-Line Identification on the PROTECT-90 Dataset: An Initial Application Benchmark](https://mlsanity.com/paper/2610.04155/) (2026)

## Abstract (arXiv)

This work introduces a latency-aware benchmarking framework for evaluating deep learning models in power system anomaly detection using high-fidelity, time-domain signals generated from an industry-grade electromagnetic transient simulator. Eight neural network architectures, ranging from MLPs to Transformers, were systematically evaluated on streaming datasets representing both physical faults and cyber-attacks in inverter-dominated networks. All models successfully classified two representative multi-event sequences in real time with sub-cycle response times below 15 ms. However, although classification decisions occurred within one cycle, the end-to-end inference latency consistently exceeded three cycles, ranging from 50 to 90 ms. These results highlight a critical gap between algorithmic capability and protection-grade deployment, pointing to the need for further optimization and hardware acceleration. The findings establish a reproducible benchmark for sub-cycle anomaly detection and provide guidance for transitioning machine learning methods from research prototypes to real-world protection applications.