Late Breaking Results: Scalable and Efficient Hyperdimensional Computing for Network Intrusion Detection
This work addresses the need for timely and efficient cyber threat detection on edge devices, offering a domain-specific improvement over prior hyperdimensional computing approaches.
The authors tackled the problem of inefficient hyperdimensional computing for network intrusion detection by proposing CyberHD, a framework that reduces dimensionality and training iterations while maintaining accuracy, achieving a 60% reduction in latency and 75% lower energy consumption compared to existing methods.
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.