Intrusion Detection in IoT Networks Using Extra Trees, Random Forests, and Hybrid Optimization Algorithms
Abstract
The Internet of Things (IoT) is essentially the physical objects that communicate over the internet with the purpose of remote measurement and control. In the area of IoT network security, it is crucial that all types of attacks on these networks are correctly recognized and that intelligent intrusion detection is done with high accuracy and at high speed. Thus, a model has been proposed in this paper to identify the intrusion of the IoT network with the help of optimized Machine Learning (ML) models. The core classifiers used here are Extra Trees (EXT) and Random Forest (RF). In the effort of enhancing the correctness of prediction, 6 various optimization algorithms, such as Grey Wolf Optimizer (GWO), Hunger Games Search (HGS), Moth-Flame Optimization (MFO), Satin Bowerbird Optimization (SBO), Slime Mould Algorithm (SMA), and Whale Optimization Algorithm (WOA), were employed for the hyperparameter (HPs) tuning of the main classifiers. The authors performed their evaluation tests on the CICIoT2023 dataset that contains over 1.19 million labeled network traffic records with 47 features and 33 different attack types. These were captured in a smart home-like IoT topology of 105 devices. The performance criteria were Accuracy, Precision, Recall, and F1-score. The EXT-MFO hybrid model from the proposed set registered the highest performance by an accuracy of 90.72% and an F1-score of 0.905. These results exceeded those of other EXT-based and RF-based variants. Finally, the accuracy of different models was also checked by performing a case study on the dataset that contains 10 cyberattacks in an IoT topology with 105 devices. The results indicated that the EXT-MFO hybrid model outperformed other models in terms of F1-score values and, therefore, was more accurate. The findings reveal that the most suitable optimizers for energy optimization of EXT and RF classifiers are the MFO and SMA algorithms, respectively. The findings also suggested that, generally, the models on the EXT classifier complemented by different optimizers are the ones that use more time for HP optimization. The results show that the EXT-MFO model outperforms all other State-of-the-Art (SOTA) optimization algorithms in the Internet of Things (IoT) Intrusion Detection (ID). Therefore, the EXT-MFO hybrid model, which is based on the research results, is put forward to find out the attempted attacks aimed at the IoT network.
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