Enhancing IoT Security: A Comparative Analysis of Hybrid Hyperparameter Optimization for Deep Learning-Based Intrusion Detection Systems
Heshamt Asadi, Mahmood Alborzi, Hessam Zandhessami · Journal of Information Systems and Telecommunication (JIST) · 2024
Rapidly expanding domains such as the Internet of Things require sophisticated approaches to securing interconnected devices against cyber threats.The following study intends to fill in a crucial gap in the state of effective intrusion detection systems for the Internet of Things based on a comparison and analysis of various hyperparameter optimization approaches to improve existin g and future detection systems.In other words, our main goa l was to investigate and compare various hyperparameter optimization strategies to find and assess the most effective way to improve the performance of deep learning -based IDS.Our methodology was comprised of the followin g comparative optimization analys is used to compare a hybrid optimization approach against stand-alone implementation of Harmony Search and Bayesian Optimization.The analysis was done quantitatively based on IDS trained and tested on simulated Internet of Things network data, and IDS performance was evaluated by the following metrics : accuracy, precision, recall, and F1 score.The comparison of results showed that the hybrid optimization demonstrated the best performance indicators in terms of accuracy at 99.74%, precision at 99.7%, reca ll at 99.72%, and F1 score at 99.71%.The results of the study confirm the efficiency of implementing multiple optimization approaches and reveal the potential effectiveness of such combination for effective hyperparameter optimization of deep learning -based IDS in the Internet of Things environment.