Enhancing IoT Security: A Comparative Analysis of Hybrid Hyperparameter Optimization for Deep Learning-Based Intrusion Detection Systems
Subject Areas : Machine learning
Heshamt Asadi
1
*
,
Mahmood Alborzi
2
,
Hessam Zandhessami
3
1 - Islamic Azad University, Science and Research Branch, Qazvin Islamic Azad University
2 - Islamic Azad University, Science and Research Branch, Qazvin Islamic Azad University
3 - Islamic Azad University, Science and Research Branch, Qazvin Islamic Azad University
Keywords: Internet of Things, Intrusion Detection System, Hyperparameter Optimization, Deep Learning, Harmony Search, Bayesian Optimization.,
Abstract :
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.
[1] H. Nandanwar and R. Katarya, “Deep learning enabled intrusion detection system for Industrial IOT environment,” Expert Syst Appl, vol. 249, p. 123808, Sep. 2024, doi: 10.1016/j.eswa.2024.123808.
[2] M. F. Elrawy, A. I. Awad, and H. F. A. Hamed, “Intrusion detection systems for IoT-based smart environments: a survey,” Journal of Cloud Computing, vol. 7, no. 1, p. 21, Dec. 2018, doi: 10.1186/s13677-018-0123-6.
[3] A. Kaushik and H. Al-Raweshidy, “A novel intrusion detection system for internet of things devices and data,” Wireless Networks, vol. 30, no. 1, pp. 285–294, Jan. 2024, doi: 10.1007/s11276-023-03435-0.
[4] S. Alosaimi and S. M. Almutairi, “An Intrusion Detection System Using BoT-IoT,” Applied Sciences, vol. 13, no. 9, p. 5427, Apr. 2023, doi: 10.3390/app13095427.
[5] V. Choudhary, S. Tanwar, and T. Choudhury, “Evaluation of contemporary intrusion detection systems for internet of things environment,” Multimed Tools Appl, vol. 83, no. 3, pp. 7541–7581, Jan. 2024, doi: 10.1007/s11042-023-15918-5.
[6] A. Awajan, “A Novel Deep Learning-Based Intrusion Detection System for IoT Networks,” Computers, vol. 12, no. 2, p. 34, Feb. 2023, doi: 10.3390/computers12020034.
[7] R. Chaganti, W. Suliman, V. Ravi, and A. Dua, “Deep Learning Approach for SDN-Enabled Intrusion Detection System in IoT Networks,” Information, vol. 14, no. 1, p. 41, Jan. 2023, doi: 10.3390/info14010041.
[8] E. Anthi, L. Williams, M. Slowinska, G. Theodorakopoulos, and P. Burnap, “A Supervised Intrusion Detection System for Smart Home IoT Devices,” IEEE Internet Things J, vol. 6, no. 5, pp. 9042–9053, Oct. 2019, doi: 10.1109/JIOT.2019.2926365.
[9] T. Saba, A. Rehman, T. Sadad, H. Kolivand, and S. A. Bahaj, “Anomaly-based intrusion detection system for IoT networks through deep learning model,” Computers and Electrical Engineering, vol. 99, p. 107810, Apr. 2022, doi: 10.1016/j.compeleceng.2022.107810.
[10] M. Almiani, A. AbuGhazleh, A. Al-Rahayfeh, S. Atiewi, and A. Razaque, “Deep recurrent neural network for IoT intrusion detection system,” Simul Model Pract Theory, vol. 101, p. 102031, May 2020, doi: 10.1016/j.simpat.2019.102031.
[11] A. Heidari and M. A. Jabraeil Jamali, “Internet of Things intrusion detection systems: a comprehensive review and future directions,” Cluster Comput, vol. 26, no. 6, pp. 3753–3780, Dec. 2023, doi: 10.1007/s10586-022-03776-z.
[12] D. Musleh, M. Alotaibi, F. Alhaidari, A. Rahman, and R. M. Mohammad, “Intrusion Detection System Using Feature Extraction with Machine Learning Algorithms in IoT,” Journal of Sensor and Actuator Networks, vol. 12, no. 2, p. 29, Mar. 2023, doi: 10.3390/jsan12020029.
[13] A. Kumar, K. Abhishek, M. R. Ghalib, A. Shankar, and X. Cheng, “Intrusion detection and prevention system for an IoT environment,” Digital Communications and Networks, vol. 8, no. 4, pp. 540–551, Aug. 2022, doi: 10.1016/j.dcan.2022.05.027.
[14] S. U. Jan, S. Ahmed, V. Shakhov, and I. Koo, “Toward a Lightweight Intrusion Detection System for the Internet of Things,” IEEE Access, vol. 7, pp. 42450–42471, 2019, doi: 10.1109/ACCESS.2019.2907965.
[15] H. Alazzam, A. Sharieh, and K. E. Sabri, “A feature selection algorithm for intrusion detection system based on Pigeon Inspired Optimizer,” Expert Syst Appl, vol. 148, p. 113249, Jun. 2020, doi: 10.1016/j.eswa.2020.113249.
[16] R. Zhao et al., “A Novel Intrusion Detection Method Based on Lightweight Neural Network for Internet of Things,” IEEE Internet Things J, vol. 9, no. 12, pp. 9960–9972, 2022, doi: 10.1109/JIOT.2021.3119055.
[17] S. M. Kasongo, “A deep learning technique for intrusion detection system using a Recurrent Neural Networks based framework,” Comput Commun, vol. 199, pp. 113–125, Feb. 2023, doi: 10.1016/j.comcom.2022.12.010.
[18] V. Hnamte, H. Nhung-Nguyen, J. Hussain, and Y. Hwa-Kim, “A Novel Two-Stage Deep Learning Model for Network Intrusion Detection: LSTM-AE,” IEEE Access, vol. 11, pp. 37131–37148, 2023, doi: 10.1109/ACCESS.2023.3266979.