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.

翻译：异常检测是智能系统的基础组成部分，应用于医疗健康、网络安全、智能电网和物联网环境。尽管传统机器学习和深度学习方法在识别异常方面展现了有效性，但它们通常依赖大规模标注数据集、计算成本高昂，并在边缘和高维场景下面临可扩展性挑战。本文提出D2H-AD，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表示信息。与现有基于HDC的方法不同，D2H-AD在统一框架中整合了基于距离的相似性和密度感知编码，从而提升了异常表示和检测性能。消融研究表明，单独使用超维编码相较于直接在原始特征空间中应用相同的密度-距离评分，ROC-AUC指标最高可提升5.4%。此外，在所有评估数据集上，D2H-AD始终优于五种既定基线方法，即HDAD、ODHD、单类支持向量机、孤立森林和自编码器。该框架轻量、可解释且计算高效，适用于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，展示了其在F1分数和ROC-AUC方面的优异性能，以及对类别不平衡、噪声和数据复杂度的鲁棒性。除精度提升外，D2H-AD还具备可扩展性、小内存占用和低延迟特性，这得益于其二进制计算和紧凑设计。这些特性使其对TinyML和边缘AI部署尤其具有吸引力。所提框架凸显了HDC在动态环境中实现精准、可解释且节能异常检测的潜力。