ResNeSt-biGRU: An Intrusion Detection Model Based on Internet of Things

The rapid expansion of Internet of Things (IoT) devices across various sectors is driven by steadily increasing demands for interconnected and smart technologies. Nevertheless, the surge in the number of IoT device has caught the attention of cyber hackers, as it provides them with expanded avenues to access valuable data. This has resulted in a myriad of security challenges, including information leakage, malware propagation, and financial loss, among others. Consequently, developing an intrusion detection system to identify both active and potential intrusion traffic in IoT networks is of paramount importance. In this paper, we propose ResNeSt-biGRU, a practical intrusion detection model that combines the strengths of ResNeSt, a variant of Residual Neural Network, and bidirectional Gated Recurrent Unit Network (biGRU). Our ResNeSt-biGRU framework diverges from conventional intrusion detection systems (IDS) by employing this dual-layered mechanism that exploits the temporal continuity and spatial feature within network data streams, a methodological innovation that enhances detection accuracy. In conjunction with this, we introduce the PreIoT dataset, a compilation of prevalent IoT network behaviors, to train and evaluate IDS models with a focus on identifying potential intrusion traffics. The effectiveness of proposed scheme is demonstrated through testing, wherein it achieved an average accuracy of 99.90% on the N-BaIoT dataset as well as on the PreIoT dataset and 94.45% on UNSW-NB15 dataset. The outcomes of this research reveal the potential of ResNeSt-biGRU to bolster security measures, diminish intrusion-related vulnerabilities, and preserve the overall security of IoT ecosystems.