Advanced AI-driven intrusion detection for securing cloud-based industrial IoT

The rapid integration of smart devices with cloud services in the Industrial Internet of Things (IIoT) has exposed significant vulnerabilities in conventional security protocols, making them insufficient against sophisticated cyber threats. Despite advancements in intrusion detection systems (IDS), there remains a critical need for highly accurate, adaptive, and scalable solutions for cloud-based IIoT environments. Motivated by this necessity, we propose an advanced AI-powered IDS leveraging Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks. Developed using Python and the Kitsune dataset, our IDS demonstrates a remarkable detection accuracy of 98.68%, a low False Negative rate of 0.01%, and an impressive F1 score of 98.62%. Comparative analysis with other deep learning models validates the superior performance of our approach. This research contributes significantly to enhancing cybersecurity in cloud-based IIoT systems, offering a robust, scalable solution to mitigate evolving cyber threats.