# Late Breaking Results: Scalable and Efficient Hyperdimensional Computing for Network Intrusion Detection

arXiv: https://arxiv.org/abs/2304.06728 · HTML: https://mlsanity.com/paper/2304.06728/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 10 references that are in the map.
- First posted on arXiv 11 Apr 2023.

## Built on by (most cited first, 5 of 5)

- [Learning from Hypervectors: A Survey on Hypervector Encoding](https://mlsanity.com/paper/2308.00685/) (2023)
- [DOMINO: Domain-invariant Hyperdimensional Classification for Multi-Sensor Time Series Data](https://mlsanity.com/paper/2308.03295/) (2023)
- [Robust and Scalable Hyperdimensional Computing With Brain-Like Neural Adaptations](https://mlsanity.com/paper/2311.07705/) (2023)
- [D2H-AD: A Hybrid Model Utilizing Hyperdimensional Computing for Advanced Anomaly Detection](https://mlsanity.com/paper/2606.13754/) (2026)
- [CRUISE: Vision-Language Model-Guided Uncertainty-Aware Cross-Modal Sensor Fusion for Robust Autonomous Driving](https://mlsanity.com/paper/2608.09202/) (2026)

## Abstract (arXiv)

Cybersecurity has emerged as a critical challenge for the industry. With the large complexity of the security landscape, sophisticated and costly deep learning models often fail to provide timely detection of cyber threats on edge devices. Brain-inspired hyperdimensional computing (HDC) has been introduced as a promising solution to address this issue. However, existing HDC approaches use static encoders and require very high dimensionality and hundreds of training iterations to achieve reasonable accuracy. This results in a serious loss of learning efficiency and causes huge latency for detecting attacks. In this paper, we propose CyberHD, an innovative HDC learning framework that identifies and regenerates insignificant dimensions to capture complicated patterns of cyber threats with remarkably lower dimensionality. Additionally, the holographic distribution of patterns in high dimensional space provides CyberHD with notably high robustness against hardware errors.