Improving Detection of Intrusion to Internet of Things Network Using Deep Learning and Chaotic Krill Optimization Algorithm

The Internet of Things is a new technology that communicates with the surrounding objects through the Internet and is used for the purpose of remote measurement and control. In the field of Internet of Things (IoT) network security, it is very important to accurately identify the types of attacks on these networks that are launched by zombie hosts under the control of the attacker. In this article, a new neural network is proposed to improve the detection of intrusion into the Internet of Things network based on the ALEXNET convolutional neural network and chaotic krill optimization algorithm (MONANET). In the MONANET network, in order to improve the accuracy in detecting intrusion into the IoT network and not need to manually adjust the parameters, the hyperparameters of the neural network are dynamically selected using the chaotic krill algorithm. The value of the loss function of the validation set obtained from the first training of the neural network model using the Danmini doorbell dataset is considered as the CKH fitness value. The comprehensive performance of the proposed network and GRU, ANN, SVM, LSTM, R-CNN, and APSO-CNN algorithms have been compared in five evaluation indices and 12 times independent experiments. The obtained results show the improvement of intrusion detection to the Internet of Things network. The proposed algorithm has been able to accurately detect %99.89 attacks on the Internet of Things network. The experimental results show the superiority of the proposed method over other knowledge boundary methods in terms of improving classification accuracy.