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 …

address brain challenge complexity computing critical cyber cybersecurity cyber threats deep learning detection devices edge edge devices efficiency fail high industry intrusion intrusion detection issue large latency loss network results security serious solution threats training