# D2H-AD: A Hybrid Model Utilizing Hyperdimensional Computing for Advanced Anomaly Detection

arXiv: https://arxiv.org/abs/2606.13754 · HTML: https://mlsanity.com/paper/2606.13754/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 60 references that are in the map.
- First posted on arXiv 11 Jun 2026.

## Builds on (most cited first, 13 of 13)

- [Classification using Hyperdimensional Computing: A Review](https://mlsanity.com/paper/2004.11204/) (2020)
- [Vector Symbolic Architectures as a Computing Framework for Emerging Hardware](https://mlsanity.com/paper/2106.05268/) (2021)
- [Brain-inspired global-local learning incorporated with neuromorphic computing](https://mlsanity.com/paper/2006.03226/) (2020)
- [Hyperdimensional Feature Fusion for Out-Of-Distribution Detection](https://mlsanity.com/paper/2112.05341/) (2021)
- [Late Breaking Results: Scalable and Efficient Hyperdimensional Computing for Network Intrusion Detection](https://mlsanity.com/paper/2304.06728/) (2023)
- [Hyperdimensional computing encoding for feature selection on the use case of epileptic seizure detection](https://mlsanity.com/paper/2205.07654/) (2022)
- [Real-Time COVID-19 Diagnosis from X-Ray Images Using Deep CNN and Extreme Learning Machines Stabilized by Chimp Optimization Algorithm](https://mlsanity.com/paper/2106.01435/) (2021)
- [RES-HD: Resilient Intelligent Fault Diagnosis Against Adversarial Attacks Using Hyper-Dimensional Computing](https://mlsanity.com/paper/2203.08148/) (2022)
- [Hybrid Efficient Unsupervised Anomaly Detection for Early Pandemic Case Identification](https://mlsanity.com/paper/2408.05347/) (2024)
- [Network Anomaly Detection for IoT Using Hyperdimensional Computing on NSL-KDD](https://mlsanity.com/paper/2503.03031/) (2025)
- [Hyperdimensional computing: a fast, robust and interpretable paradigm for biological data](https://mlsanity.com/paper/2402.17572/) (2024)
- [Real-Time Automated donning and doffing detection of PPE based on Yolov4-tiny](https://mlsanity.com/paper/2407.17471/) (2024)
- [Intrusion Detection in IoT Networks Using Hyperdimensional Computing: A Case Study on the NSL-KDD Dataset](https://mlsanity.com/paper/2503.03037/) (2025)

## Abstract (arXiv)

Anomaly detection is a fundamental component of intelligent systems with applications in healthcare, cybersecurity, smart grids, and IoT environments. Although conventional machine learning and deep learning methods have demonstrated effectiveness in identifying anomalies, they often rely on large labeled datasets, incur high computational costs, and face scalability challenges in edge and high-dimensional settings. This paper presents D2H-AD, a novel anomaly detection framework based on Hyperdimensional Computing (HDC), a brain-inspired paradigm that represents information using high-dimensional distributed vectors. Unlike existing HDC-based methods, D2H-AD integrates distance-based similarity and density-aware encoding within a unified framework, improving anomaly representation and detection performance. Ablation studies show that hyperdimensional encoding alone yields up to 5.4% higher ROC-AUC than applying the same density-distance scoring directly in the original feature space. Furthermore, D2H-AD consistently outperforms five established baselines, namely HDAD, ODHD, One-Class SVM, Isolation Forest, and Autoencoders, across all evaluated datasets. The framework is lightweight, interpretable, and computationally efficient, making it suitable for resource-constrained and real-time applications. We validate D2H-AD on five benchmark datasets and demonstrate superior F1-score and ROC-AUC performance, together with robustness to class imbalance, noise, and data complexity. In addition to improved accuracy, D2H-AD offers scalability, a small memory footprint, and low-latency operation enabled by binary computations and a compact design. These properties make it particularly attractive for TinyML and edge AI deployments. The proposed framework highlights the potential of HDC for accurate, interpretable, and energy-efficient anomaly detection in dynamic environments.