An improved cyber-attack detection and classification model for the internet of things systems using fine-tuned deep learning model

Internet of things (IoT) networks increasingly need security due to the large amount of data that needs to be managed. These networks are susceptible to a variety of sophisticated and more frequent cyberattacks. In this study, an improved cyber-attack detection model is presented for IoT networks using a fine-tuned deep learning model. This model produces high accuracy and classifies the different types of cyber-attacks with low losses. In the feature selection process, a wrapper-based dwarf mongoose optimisation algorithm (W-DMO) is utilised to choose the best subset of features from the original network traffic features. Lastly, a hybrid triple attention deep neural network-assisted BiLSTM model (TDeepBiL) is employed to classify the features and categorise different kinds of attacks. Several performance metrics are evaluated for the proposed method, including accuracy, precision, recall, and F1-score. The proposed model has reached a high accuracy of 99.44% for the UNSW-NB 15 dataset and 98.6% for the ToN-IoT dataset in comparison to other current models. Thus, the presented model gains significant improvement in cyber-attack detection.