<p>The increasing integration of cyber-physical systems (CPSs) and information and communication technologies (ICT) within the Smart Grid (SG) framework has led to significant advancements in energy systems. However, this integration introduces vulnerabilities, particularly cyberattacks like distributed denial of service (DDoS) attacks. This research presents a new approach to detecting cyberattacks in SG by combining deep learning (DL) techniques with the whale optimization (WOA) and fisher mantis optimization (FMO) algorithm, which forms the WOA-FMO hybrid algorithm. The system utilizes convolutional neural networks (CNN) for feature extraction and long-short-term memory (LSTM) networks to classify network traffic into normal and abnormal categories. The WOA-FMO algorithm optimizes the feature selection process, reducing dimensionality and improving model accuracy, thereby enhancing detection efficiency. Experimental evaluations on the PhishTank, UCI, and Tan datasets demonstrate that the proposed approach outperforms traditional approaches in terms of sensitivity, specificity, accuracy, and precision. A comparison of five optimization algorithms—GOA, ABC, BWO, GWO, and WOA-FMO—reveals that the WOA-FMO hybrid achieves the highest sensitivity (98.86%), accuracy (98.57%), and precision (98.30%), as well as strong specificity (98.28%). These results underscore the effectiveness of WOA-FMO in optimizing feature selection and improving classification performance, offering a robust solution for enhancing the resilience of IoT-based SGs against advanced cyberattacks.</p>