# RES-HD: Resilient Intelligent Fault Diagnosis Against Adversarial Attacks Using Hyper-Dimensional Computing

arXiv: https://arxiv.org/abs/2203.08148 · HTML: https://mlsanity.com/paper/2203.08148/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 45 references that are in the map.
- First posted on arXiv 14 Mar 2022.

## Builds on (most cited first, 9 of 9)

- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Towards Deep Learning Models Resistant to Adversarial Attacks](https://mlsanity.com/paper/1706.06083/) (2017)
- [Adversarial examples in the physical world](https://mlsanity.com/paper/1607.02533/) (2016)
- [Adversarial Attacks and Defences: A Survey](https://mlsanity.com/paper/1810.00069/) (2018)
- [Adversarial Attacks on Deep Neural Networks for Time Series Classification](https://mlsanity.com/paper/1903.07054/) (2019)
- [A Survey of Black-Box Adversarial Attacks on Computer Vision Models](https://mlsanity.com/paper/1912.01667/) (2019)
- [Crafting Adversarial Examples for Deep Learning Based Prognostics (Extended Version)](https://mlsanity.com/paper/2009.10149/) (2020)
- [SynergicLearning: Neural Network-Based Feature Extraction for Highly-Accurate Hyperdimensional Learning](https://mlsanity.com/paper/2007.15222/) (2020)
- [Spiking Hyperdimensional Network: Neuromorphic Models Integrated with Memory-Inspired Framework](https://mlsanity.com/paper/2110.00214/) (2021)

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

- [Learning from Hypervectors: A Survey on Hypervector Encoding](https://mlsanity.com/paper/2308.00685/) (2023)
- [DODEM: DOuble DEfense Mechanism Against Adversarial Attacks Towards Secure Industrial Internet of Things Analytics](https://mlsanity.com/paper/2301.09740/) (2023)
- [D2H-AD: A Hybrid Model Utilizing Hyperdimensional Computing for Advanced Anomaly Detection](https://mlsanity.com/paper/2606.13754/) (2026)

## Abstract (arXiv)

Industrial Internet of Things (I-IoT) enables fully automated production systems by continuously monitoring devices and analyzing collected data. Machine learning methods are commonly utilized for data analytics in such systems. Cyber-attacks are a grave threat to I-IoT as they can manipulate legitimate inputs, corrupting ML predictions and causing disruptions in the production systems. Hyper-dimensional computing (HDC) is a brain-inspired machine learning method that has been shown to be sufficiently accurate while being extremely robust, fast, and energy-efficient. In this work, we use HDC for intelligent fault diagnosis against different adversarial attacks. Our black-box adversarial attacks first train a substitute model and create perturbed test instances using this trained model. These examples are then transferred to the target models. The change in the classification accuracy is measured as the difference before and after the attacks. This change measures the resiliency of a learning method. Our experiments show that HDC leads to a more resilient and lightweight learning solution than the state-of-the-art deep learning methods. HDC has up to 67.5% higher resiliency compared to the state-of-the-art methods while being up to 25.1% faster to train.