SecureCPS-Opt: A Hybrid Optimization And Federated AI Framework For Efficient And Privacy-Preserving Attack Detection In IoT-Enabled Cyber-Physical Systems

The rapid growth of Internet of Things (IoT) devices has significantly improved automation, connectivity, and data-driven decision-making across various domains such as healthcare, smart cities, agriculture, and industrial systems. However, the increasing number of interconnected devices has also introduced serious security challenges. IoT-enabled cyber-physical systems are highly vulnerable to cyber-attacks such as Distributed Denial of Service (DDoS), data injection, botnet attacks, and unauthorized access. Traditional machine learning techniques often struggle to provide high detection accuracy due to imbalanced datasets, high-dimensional features, and inefficient parameter tuning. In this project, a hybrid deep learning-based intrusion detection framework is proposed for identifying security attacks in IoT-enabled cyber-physical systems. The proposed model combines Convolutional Neural Network (CNN) and Deep Belief Network (DBN) to improve feature learning and classification performance. To enhance the model's efficiency and convergence speed, a novel hybrid optimization technique called Seagull Adopted Elephant Herding Optimization (SAEHO) is employed for tuning the classifier weights. The proposed framework is evaluated using standard IoT intrusion detection datasets such as UNSW-NB15 and BoT-IoT. Performance is measured using metrics including accuracy, precision, sensitivity, specificity, False Positive Rate (FPR), False Negative Rate (FNR), and Matthews Correlation Coefficient (MCC). Experimental results demonstrate that the hybrid classifier optimized using SAEHO outperforms conventional machine learning and optimization-based models in terms of detection accuracy and reduced error rates. The developed system provides an effective and scalable solution for enhancing security in IoT-enabled cyber-physical environments.