# A Single-Point Measurement Framework for Robust Cyber-Attack Diagnosis in Smart Microgrids Using Dual Fractional-Order Feature Analysis

arXiv: https://arxiv.org/abs/2507.06890 · HTML: https://mlsanity.com/paper/2507.06890/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 32 references that are in the map.
- First posted on arXiv 9 Jul 2025.

## Builds on (most cited first, 3 of 3)

- [Towards Deep Learning Models Resistant to Adversarial Attacks](https://mlsanity.com/paper/1706.06083/) (2017)
- [Curriculum Adversarial Training](https://mlsanity.com/paper/1805.04807/) (2018)
- [Impacts of Bad Data on the PMU based Line Outage Detection](https://mlsanity.com/paper/1502.04236/) (2015)

## Abstract (arXiv)

Cyber-attacks jeopardize the safe operation of smart microgrids. At the same time, existing diagnostic methods either depend on expensive multi-point instrumentation or stringent modelling assumptions that are untenable under single-sensor constraints. This paper proposes a Fractional-Order Memory-Enhanced Attack-Diagnosis Scheme (FO-MADS) that achieves low-latency fault localisation and cyber-attack detection using only one VPQ (Voltage-Power-Reactive-power) sensor. FO-MADS first constructs a dual fractional-order feature library by jointly applying Caputo and Gr"unwald-Letnikov derivatives, thereby amplifying micro-perturbations and slow drifts in the VPQ signal. A two-stage hierarchical classifier then pinpoints the affected inverter and isolates the faulty IGBT switch, effectively alleviating class imbalance. Robustness is further strengthened through Progressive Memory-Replay Adversarial Training (PMR-AT), whose attack-aware loss is dynamically re-weighted via Online Hard Example Mining (OHEM) to prioritise the most challenging samples. Experiments on a four-inverter microgrid testbed comprising 1 normal and 24 fault classes under four attack scenarios demonstrate diagnostic accuracies of 96.6 % (bias), 94.0 % (noise), 92.8 % (data replacement), and 95.7 % (replay), while sustaining 96.7 % under attack-free conditions. These results establish FO-MADS as a cost-effective and readily deployable solution that markedly enhances the cyber-physical resilience of smart microgrids.