Abstract

Network-based security has emerged as an increasingly critical challenge in the domain of the Internet of Things (IoT). A number of network intrusion detection systems (NIDS), typically relying on sophisticated machine learning (ML) algorithms, have been proposed to monitor network traffic and detect malicious activity. However, these NIDS designs require extensive memory and computational power, exceeding the capability of today’s IoT devices, and often fail to provide timely detection of network attacks. To tackle this issue, we propose mathsf HyperDetect , the first attempt at NIDS modeling that leverages the highly efficient and parallel operations of brain-inspired hyperdimensional computing (HDC). Our innovative model updating method effectively mitigates model saturation and significantly reduces the number of retraining iterations needed to reach convergence. Additionally, we employ a novel dynamic encoding technique to regenerate insignificant dimensions, considerably lowering the dimensionalities required to achieve high-quality performance and further accelerating the learning process. mathsf HyperDetect delivers on average 5.02times faster training and 31.83times faster inference compared to state-of-the-art (SOTA) learning approaches on a wide range of network intrusion classification tasks. We also extensively evaluate mathsf HyperDetect on embedded hardware to demonstrate its low-latency and resource-efficient characteristics.