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

D2H-AD：一种利用超维计算进行高级异常检测的混合模型

Abstract: 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.

摘要： 异常检测是智能系统的核心组成部分，广泛应用于医疗保健、网络安全、智能电网和物联网环境。尽管传统的机器学习和深度学习方法在识别异常方面表现出了一定的有效性，但它们通常依赖于大规模标注数据集，计算成本高昂，且在边缘计算和高维场景中面临可扩展性挑战。

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.

本文提出了 D2H-AD，这是一种基于超维计算（HDC）的新型异常检测框架。HDC 是一种受大脑启发的范式，利用高维分布式向量来表示信息。与现有的基于 HDC 的方法不同，D2H-AD 将基于距离的相似度与密度感知编码集成在一个统一的框架内，从而提升了异常表示和检测性能。

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.

消融实验表明，仅使用超维编码所获得的 ROC-AUC 比直接在原始特征空间应用相同的密度-距离评分高出 5.4%。此外，在所有评估的数据集上，D2H-AD 的表现均持续优于五种既定基准模型，即 HDAD、ODHD、One-Class SVM、孤立森林（Isolation Forest）和自动编码器（Autoencoders）。

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.

该框架轻量、可解释且计算高效，非常适合资源受限和实时应用场景。我们在五个基准数据集上验证了 D2H-AD，结果显示其在 F1 分数和 ROC-AUC 性能上表现卓越，同时对类别不平衡、噪声和数据复杂性具有较强的鲁棒性。

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.

除了精度的提升，D2H-AD 还具备可扩展性、内存占用小以及得益于二进制计算和紧凑设计所带来的低延迟运行优势。这些特性使其在 TinyML 和边缘 AI 部署中极具吸引力。该框架凸显了 HDC 在动态环境中实现精确、可解释且节能的异常检测的巨大潜力。