A novel lightweight deep learning framework using enhanced pelican optimization for efficient cyberattack detection in the Internet of Things environments

Abstract The extensive use of Internet of Things (IoT) technology produces unprecedented connectivity and cyberattack exposure. Recent attack detection tools have poor accuracy, efficiency, and adaptability in the case of IoT systems with scarce resources. To counter these challenges, the current study proposes a hybrid model incorporating an efficient convolutional neural network (CNN) and an enhanced pelican optimization algorithm (EPOA) to detect IoT network attacks. Inspired by how pelicans hunt, EPOA maximizes CNN’s hyperparameters and feature selection for higher accuracy and efficiency in computation. Experimentation with the Bot-IoT, CICIDS2018, and NSL-KDD datasets validates the performance of the proposed EPOA-based deep learning method for cyberattack detection. The model achieves 98.1% accuracy on Bot-IoT, 97.4% on NSL-KDD, and 97.9% on CICIDS2018, outperforming conventional approaches like long short-term memory (LSTM), gated recurrent unit (GRU), support vector machine (SVM), logistic regression (LR), artificial neural network (ANN), and recurrent neural network (RNN). The model also produces a minimum loss value of 0.17, outperforming other approaches with the shortest execution duration. With its efficient design and high detection performance, the proposed approach is highly suitable for continuous IoT cyberattack detection in practical deployment scenarios.