A novel hybrid optimization enabled robust CNN algorithm for an IoT network intrusion detection approach

Due to the huge number of connected Internet of Things (IoT) devices within a network, denial of service and flooding attacks on networks are on the rise. IoT devices are disrupted and denied service because of these attacks. In this study, we proposed a novel hybrid meta-heuristic adaptive particle swarm optimization-whale optimizer algorithm (APSO-WOA) for optimization of the hyperparameters of a convolutional neural network (APSO-WOA-CNN). The APSO-WOA optimization algorithm's fitness value is defined as the validation set's cross-entropy loss function during CNN model training. In this study, we compare our optimization algorithm with other optimization algorithms, such as the APSO algorithm, for optimization of the hyperparameters of CNN. In model training, the APSO-WOA-CNN algorithm achieved the best performance compared to the FNN algorithm, which used manual parameter settings. We evaluated the APSO-WOA-CNN algorithm against APSO-CNN, SVM, and FNN. The simulation results suggest that APSO-WOA-CNf[N is effective and can reliably detect multi-type IoT network attacks. The results show that the APSO-WOA-CNN algorithm improves accuracy by 1.25%, average precision by 1%, the kappa coefficient by 11%, Hamming loss by 1.2%, and the Jaccard similarity coefficient by 2%, as compared to the APSO-CNN algorithm, and the APSO-CNN algorithm achieves the best performance, as compared to other algorithms.