With the rapid growth of IoT devices, ensuring robust network security has become a critical challenge. Traditional intrusion detection systems (IDSs) often face limitations in detecting sophisticated attacks within high-dimensional and complex data environments. This paper presents a novel approach to network anomaly detection using hyperdimensional computing (HDC) techniques, specifically applied to the NSL-KDD dataset. The proposed method leverages the efficiency of HDC in processing large-scale data to identify both known and unknown attack patterns. The model achieved an accuracy of 91.55% on the KDDTrain+ subset, outperforming traditional approaches. These comparative evaluations underscore the model's superior performance, highlighting its potential in advancing anomaly detection for IoT networks and contributing to more secure and intelligent cybersecurity solutions.
@article{arxiv.2503.03031,
title = {Network Anomaly Detection for IoT Using Hyperdimensional Computing on NSL-KDD},
author = {Ghazal Ghajari and Ashutosh Ghimire and Elaheh Ghajari and Fathi Amsaad},
journal= {arXiv preprint arXiv:2503.03031},
year = {2025}
}