Intelligent Threat Detection in CPS-IoT Networks Using A Hybrid CNN-DBN Model with Saeho Optimization

The Internet of Things (IoT) is integral to smart cities and diverse societal applications, yet its large-scale implementation is hindered by significant security vulnerabilities and cyber threats. Conventional security measures frequently struggle to tackle the distinct challenges associated with IoT-driven cyber-physical systems, highlighting the need for advanced techniques like Deep Learning (DL) for robust anomaly detection. This research introduces an innovative framework that utilizes a hybrid classification strategy by combining a Deep Belief Network (DBN) with a Convolutional Neural Network (CNN). To enhance detection accuracy, the framework incorporates an innovative optimization technique called Seagull Adapted Elephant Herding Optimization (SAEHO). The "Hybrid Classifier + SAEHO" model processes extracted features from network traffic data, effectively distinguishing between malicious and benign activity. Experimental evaluations on two datasets demonstrate superior performance in terms of sensitivity, precision, accuracy, and specificity when compared to conventional methods. These results highlight the model’s potential in fortifying IoT security and offering a reliable mechanism for mitigating cyber threats in real-world applications.