“…The table compares the models in terms of learning approach, number of classes, detection accuracy, detection precision, and detection recall. Furthermore, the table considers the comparison of the proposed model with six other models, including (1) Roh et al model [41], which is implemented using a hybrid deep learning technique comprising the use of the convolutional neural network (CNN) along with the long, short-term memory (LSTM); (2) Tariq et al model [42], which is called CANTransfer and implemented using the transfer learning technique of deep cascaded model comprising several CNN-LSTM units; (3) Javed et al model [43], which is called CANintelliIDS and implemented using convolutional attention incorporated with gated recurrent neural network (GRU); (4) Song et al model [44], which is implemented using a deep convolutional neural network (DCNN); (5) Kang et al model [45], which is implemented by incorporating the deep neural networks with deep belief networks (DNN-DBN); and finally, (6) Seo et al model [46], which is called GIDS-CNN (Generative Adversarial Nets IDS -CNN). According to the table, the proposed model outperforms others in several performance indicators.…”