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

Anomaly detection is a cornerstone of modern intelligent systems, with critical applications in healthcare, cybersecurity, smart grids, and IoT environments. While traditional machine learning and deep learning models have shown promise in identifying outliers, they often face challenges such as dependence on large labeled datasets, high computational costs, and limited scalability to edge devices or high-dimensional data streams. This study introduces D2H-AD, a novel anomaly detection framework built upon Hyperdimensional Computing (HDC), a brain-inspired paradigm that encodes information using high-dimensional distributed vectors. Unlike prior HDC-based approaches, D2H-AD fuses distance-based similarity and density-aware encoding in a hybrid design, significantly improving anomaly characterization and detection accuracy. Ablation experiments confirm that hyperdimensional encoding alone contributes up to 5.4% higher ROC-AUC compared to applying the same density-distance scoring in the original Euclidean feature space, and D2H-AD consistently outperforms all five published baselines (HDAD, ODHD, One-Class SVM, Isolation Forest, and Autoencoders) across every evaluated dataset. The method is designed to be lightweight, interpretable, and computationally efficient, suggesting strong suitability for deployment in resource-constrained and real-time environments. To validate its effectiveness, we evaluate D2H-AD on five diverse benchmark datasets, comparing its performance against five leading techniques: HDAD, ODHD, One-Class SVM, Isolation Forest, and Autoencoders. Our results show that D2H-AD consistently achieves superior F1 scores and ROC-AUC metrics, demonstrating robustness against class imbalance, noise, and data complexity. Beyond accuracy, D2H-AD offers practical advantages including scalability, minimal memory footprint, and an expected low-latency profile derived from its binary operations and lightweight design. These characteristics are crucial for TinyML and edge AI applications. This work highlights the untapped potential of HDC for high-performance anomaly detection and opens new avenues for secure, interpretable, and energy-efficient AI solutions in dynamic environments such as IoT, embedded systems, and beyond. Additionally, D2H-AD provides feature-level interpretability through hypervector decoding, enabling transparent explanations for safety-critical applications.