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.

翻译：网络安全已成为工业界面临的严峻挑战。由于安全态势的高度复杂性，复杂且昂贵的深度学习模型往往无法在边缘设备上及时检测网络威胁。受大脑启发的超维计算（HDC）被提出作为解决该问题的有前景方案。然而，现有HDC方法使用静态编码器，需要极高的维度和数百次训练迭代才能达到合理精度，导致学习效率严重下降并造成攻击检测的巨大延迟。本文提出CyberHD这一创新HDC学习框架，通过识别并重构非显著性维度，以显著更低的维度捕获网络威胁的复杂模式。此外，高维空间中模式的全息分布特性使CyberHD对硬件错误具有显著的高鲁棒性。