AN ADAPTIVE SPATIOTEMPORAL DEFENSE FRAMEWORK FOR ROBUST AND EFFICIENT INTRUSION DETECTION IN IOT NETWORKS

Abstract

The ever-increasing growth rate of the Internet of Things (IoT) has significantly increased network heterogeneity and data volumes, which have posed a significant risk of various cyber threats to resource-constrained IoT devices. Traditional Intrusion Detection Systems (IDS) may not perform well in such environments, which may be characterized by noisy data, class imbalance, and a large number of feature redundancies. This study proposes an Adaptive Spatiotemporal Defense Framework suitable for real-world IoT networks. The framework includes an efficient nonlinear data preprocessing approach, discriminative feature optimization, and a spatiotemporal learning framework to improve the performance of intrusion detection systems. The framework efficiently addresses the class imbalance problem, reduces redundant features, and maintains discriminative features, thereby improving the quality of the features and reducing the complexity. The inclusion of spatiotemporal learning in the framework enables the detection of complex data relationships with high precision. The performance of the proposed approach is evaluated on three benchmarking datasets: UNSW-NB15, ToN-IoT, and IoT-23. The accuracy obtained on these datasets is 89.61%, 97.94%, and 99.99%, respectively. Along with this, high F1-score results are obtained. This proves the enhancement in intrusion detection ability. Overall, the proposed framework proves to be a reliable, efficient, and lightweight solution for intrusion detection in heterogeneous IoT environments.