Spatial Data Transformation and Vision Learning for Elevating Intrusion Detection in IoT Networks

Network intrusion detection systems (NIDSs) are vital for identifying security attacks and predicting early invasion attempts, which is essential for protecting the Internet. Recently, deep learning (DL) has made significant achievements in enhancing intrusion detection accuracy. Nevertheless, the practical implementation of high-complexity DL models is limited by the constrained computational capabilities of the Internet of Things (IoT) devices, e.g., home routers and IoT gateways. This article introduces a novel NIDS approach explicitly tailored for IoT networks, leveraging a lightweight DL model. During the data preprocessing phase, we use a spatially enriched data conversion technique to decrease the dimensionality of high-dimensional raw traffic variables. This helps to offset the problem of increased model complexity. Furthermore, when spatial relationships often exist in the data, we can simplify the learning architecture by utilizing state-of-the-art vision transformer techniques in the computer vision field that can substantially reduce model complexity. The experimental results indicate that the proposed method achieves outstanding accuracy up to 99.57% with high-volume traffic input. Moreover, the proposed method reaches substantial reductions in learnable parameters by 55.35% and 82.07%, along with a remarkable decrease in floating point operations (FLOPs) by 93.56% and 99.28% compared to existing studies. The outstanding achievement highlights the proposed method’s ability to balance model complexity and accuracy performance, making it extremely appropriate for deployment on IoT gateways with limited resources.