Enhancing IoT Security: A Comparative Analysis of Hybrid Hyperparameter Optimization for Deep Learning-Based Intrusion Detection Systems
محورهای موضوعی : Machine learning
Heshamt Asadi
1
*
,
Mahmood Alborzi
2
,
Hessam Zandhessami
3
1 - 1.Department of Management and Economics, Science and Research Branch, Islamic Azad University, Tehran, Iran.
2 - 1.Department of Management and Economics, Science and Research Branch, Islamic Azad University, Tehran, Iran.
3 - 1.Department of Management and Economics, Science and Research Branch, Islamic Azad University, Tehran, Iran.
کلید واژه: Internet of Things, Intrusion Detection System, Hyperparameter Optimization, Deep Learning, Harmony Search, Bayesian Optimization.,
چکیده مقاله :
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 existing and future detection systems. In other words, our main goal 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 following comparative optimization analysis 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%, recall 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.
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 existing and future detection systems. In other words, our main goal 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 following comparative optimization analysis 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%, recall 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.
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