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Cybersecurity has emerged as a critical challenge for the industry. With thelarge complexity of the security landscape, sophisticated and costly deeplearning models often fail to provide timely detection of cyber threats on edgedevices. Brain-inspired hyperdimensional computing (HDC) has been introduced asa promising solution to address this issue. However, existing HDC approachesuse static encoders and require very high dimensionality and hundreds oftraining iterations to achieve reasonable accuracy. This results in a seriousloss of learning efficiency and causes huge latency for detecting attacks. Inthis paper, we propose CyberHD, an innovative HDC learning framework thatidentifies and regenerates insignificant dimensions to capture complicatedpatterns of cyber threats with remarkably lower dimensionality. Additionally,the holographic distribution of patterns in high dimensional space providesCyberHD with notably high robustness against hardware errors.

Late Breaking Results: Scalable and Efficient Hyperdimensional Computing for Network Intrusion Detection