Predictive IoT-AI Model for Cyber Threat Detection in Smart Healthcare Environments

The rise of IoT-based smart healthcare environments has escalated the demand for robust and efficient cyber threat detection mechanisms, given the critical nature of these systems and their susceptibility to evolving cyber-attacks. This research presents the design of a predictive IoT-AI approach for cyber threat detection, leveraging the NSL-KDD dataset for comprehensive training and evaluation. Moreover, the existing methods demonstrates excessive computational time due to its high complexity in feature extraction and sequence modeling. In this study, the proposed model combines Convolutional Neural Network (CNN) layers for spatial feature extraction with a Gated Recurrent Unit (GRU) in the intermediate layers to capture temporal patterns and evolving threat behaviours. The combination of CNN and GRU utilizes the benefits of both models: CNNs for precise feature representation and GRUs for sequence modeling, thereby enabling the identification of sophisticated and emerging cyber threats. This hybrid architecture is optimized to attain high accuracy while retaining computational efficiency, ensuring real-time applicability in IoT-enabled healthcare systems. Through meticulous design and rigorous testing, the proposed algorithm achieved an impressive accuracy of 98%, underlining its capability to effectively and reliably detect a broad spectrum of cyber threats. The experimental results not only validate the efficacy of the CNN-GRU hybrid model but also highlight its scalability and robustness in real-world healthcare IoT applications. This exceptional accuracy underscores the model’s ability as a practical and dependable solution for safeguarding sensitive patient data and critical medical infrastructures against evolving cybersecurity challenges.