Late Breaking Results: Scalable and Efficient Hyperdimensional Computing for Network Intrusion Detection. (arXiv:2304.06728v1 [cs.CR])

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.