Metaheuristic Optimization Algorithms for Deep Learning Model Design in Secure Internet of Things Environment

Zwayyer, Mustafa; Dawood, Sajida; , and

doi:10.25673/121008

Proceedings of International Conference on Applied Innovation in IT · 2025/07/26 · Vol. 13 · Issue 3 · pp. 157–164

Metaheuristic Optimization Algorithms for Deep Learning Model Design in Secure Internet of Things Environment

Mustafa Hussein Zwayyer, Sajida Allawi Dawood, and Ammar Bassem Saleh

📄 Download PDF DOI: 10.25673/121008

Abstract

The Internet of Things (IoT) has enabled smart systems, but it has also increased vulnerabilities to cyber threats, including botnet attacks. To address these security challenges, this study proposes a hybrid system that combines metaheuristic and machine learning. To tune hyperparameters of a hybrid neural network based on Convolutional Neural Networks and Semi-Recurrent Neural Networks (CNN-QRNN), the Chaotic Butterfly Optimization Algorithm (CBOA) is used. A new metaheuristic algorithm, Self-Adaptive Enhanced Harris Hawks Optimization (SAEHO), as well as a self-upgraded cat and mouse optimizer (SU-CMO), are introduced and evaluated in order to enhance model effectiveness. Based on experiments conducted on the N-BaIoT dataset, it was determined that the proposed models significantly outperformed conventional classifiers in key performance metrics, including accuracy, the Matthews Correlation Coefficient (MCC), the Rand Index, and the F-Measure. Particularly notable improvements were observed in reducing false-positive rates and enhancing anomaly detection sensitivity. The HMMLB-BND method substantially improves detection performance in diverse IoT environments, offering a robust, efficient, and scalable solution suitable for real-time deployment in resource-constrained systems.

Keywords

Botnet Detection Internet of Things (IoT) Metaheuristic Optimization Deep Learning Feature Selection.

References

P. Rani, P. N. Singh, S. Verma, N. Ali, P. K. Shukla, and M. Alhassan, “An implementation of modified blowfish technique with honey bee behavior optimization for load balancing in cloud system environment,” Wireless Communications and Mobile Computing, vol. 2022, pp. 1–14, 2022.
Z. Chen, “Research on Internet Security Situation Awareness Prediction Technology Based on Improved RBF Neural Network Algorithm,” Journal of Computer and Cognitive Engineering, vol. 1, no. 3, pp. 103–108, Mar. 2022, doi: 10.47852/bonviewJCCE149145205514.
K. Shinan, K. Alsubhi, A. Alzahrani, and M. U. Ashraf, “Machine Learning-Based Botnet Detection in Software-Defined Network: A Systematic Review,” Symmetry, vol. 13, no. 5, p. 866, May 2021, doi: 10.3390/sym13050866.
S. Namasudra, R. G. Crespo, and S. Kumar, “Introduction to the special section on advances of machine learning in cybersecurity (VSI-mlsec),” Computers & Electrical Engineering, vol. 100, p. 108048, May 2022, doi: 10.1016/j.compeleceng.2022.108048.
A. Gutub, “Boosting image watermarking authenticity spreading secrecy from counting-based secret-sharing,” CAAI Transactions on Intelligent Technology, vol. 8, no. 2, pp. 440–452, Jun. 2023, doi: 10.1049/cit2.12093.
S. Das and S. Namasudra, “Multiauthority CP-ABE-based Access Control Model for IoT-enabled Healthcare Infrastructure,” IEEE Transactions on Industrial Informatics, vol. 19, no. 1, pp. 821–829, Jan. 2023, doi: 10.1109/TII.2022.3167842.
B. Bhola et al., “Quality-enabled decentralized dynamic IoT platform with scalable resources integration,” IET Communications, 2022.
A. A. Laghari, A. A. Khan, R. Alkanhel, H. Elmannai, and S. Bourouis, “Lightweight-BIoV: Blockchain Distributed Ledger Technology (BDLT) for Internet of Vehicles (IoVs),” Electronics, vol. 12, no. 3, p. 677, Jan. 2023, doi: 10.3390/electronics12030677.
M. Waqas et al., “Botnet attack detection in Internet of Things devices over cloud environment via machine learning,” Concurrency and Computation: Practice and Experience, vol. 34, no. 4, p. e6662, Feb. 2022, doi: 10.1002/cpe.6662.
A. A. Laghari, X. Zhang, Z. A. Shaikh, A. Khan, V. V. Estrela, and S. Izadi, “A review on quality of experience (QoE) in cloud computing,” Journal of Reliable Intelligent Environments, vol. 10, no. 2, pp. 107–121, Jun. 2024, doi: 10.1007/s40860-023-00210-y.
P. Rani and R. Sharma, “Intelligent transportation system for internet of vehicles based vehicular networks for smart cities,” Computers & Electrical Engineering, vol. 105, p. 108543, 2023.
A. Wani, R. S., and R. Khaliq, “SDN-based intrusion detection system for IoT using deep learning classifier (IDSIoT-SDL),” CAAI Transactions on Intelligent Technology, vol. 6, no. 3, pp. 281–290, Sep. 2021, doi: 10.1049/cit2.12003.
P. Rani et al., “Federated Learning-Based Misbehavior Detection for the 5G-Enabled Internet of Vehicles,” IEEE Transactions on Consumer Electronics, vol. 70, no. 2, pp. 4656–4664, May 2024, doi: 10.1109/TCE.2023.3328020.
F. Sattari, A. H. Farooqi, Z. Qadir, B. Raza, H. Nazari, and M. Almutiry, “A Hybrid Deep Learning Approach for Bottleneck Detection in IoT,” IEEE Access, vol. 10, pp. 77039–77053, 2022, doi: 10.1109/ACCESS.2022.3188635.
N. Hussain and P. Rani, “Comparative studied based on attack resilient and efficient protocol with intrusion detection system based on deep neural network for vehicular system security,” in Distributed Artificial Intelligence, CRC Press, 2020, pp. 217–236.
P. Rani and R. Sharma, “IMFOCA-IOV: Intelligent Moth Flame Optimization based Clustering Algorithm for Internet of Vehicle,” in 2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT), IEEE, 2023, pp. 1–6.
S. Mirjalili and A. Lewis, “The whale optimization algorithm,” Advances in Engineering Software, vol. 95, pp. 51–67, 2016.
S. Mirjalili, S. M. Mirjalili, and A. Lewis, “Grey Wolf Optimizer,” Advances in Engineering Software, vol. 69, pp. 46–61, Mar. 2014, doi: 10.1016/j.advengsoft.2013.12.007.
S. Mirjalili, A. H. Gandomi, S. Z. Mirjalili, S. Saremi, H. Faris, and S. M. Mirjalili, “Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems,” Advances in Engineering Software, vol. 114, pp. 163–191, Dec. 2017, doi: 10.1016/j.advengsoft.2017.07.002.
H. Faris et al., “An efficient binary Salp Swarm Algorithm with crossover scheme for feature selection problems,” Knowledge-Based Systems, vol. 154, pp. 43–67, Aug. 2018, doi: 10.1016/j.knosys.2018.05.009.
M. Tubishat, N. Idris, L. Shuib, M. A. M. Abushariah, and S. Mirjalili, “Improved Salp Swarm Algorithm based on opposition based learning and novel local search algorithm for feature selection,” Expert Systems with Applications, vol. 145, p. 113122, May 2020, doi: 10.1016/j.eswa.2019.113122.
A. Elsaeidy, K. S. Munasinghe, D. Sharma, and A. Jamalipour, “Intrusion detection in smart cities using Restricted Boltzmann Machines,” Journal of Network and Computer Applications, vol. 135, pp. 76–83, Jun. 2019, doi: 10.1016/j.jnca.2019.02.026.
Z. Baig, N. Syed, and N. Mohammad, “Securing the Smart City Airspace: Drone Cyber Attack Detection through Machine Learning,” Future Internet, vol. 14, no. 7, p. 205, Jun. 2022, doi: 10.3390/fi14070205.
N. Hussain, P. Rani, N. Kumar, and M. G. Chaudhary, “A deep comprehensive research architecture, characteristics, challenges, issues, and benefits of routing protocol for vehicular ad-hoc networks,” International Journal of Distributed Systems and Technologies (IJDST), vol. 13, no. 8, pp. 1–23, 2022.
M. Aloqaily, S. Otoum, I. A. Ridhawi, and Y. Jararweh, “An intrusion detection system for connected vehicles in smart cities,” Ad Hoc Networks, vol. 90, p. 101842, Jul. 2019, doi: 10.1016/j.adhoc.2019.02.001.
M. Shafiq, Z. Tian, Y. Sun, X. Du, and M. Guizani, “Selection of effective machine learning algorithm and Bot-IoT attacks traffic identification for internet of things in smart city,” Future Generation Computer Systems, vol. 107, pp. 433–442, 2020.
A. A. Elsaeidy, A. Jamalipour, and K. S. Munasinghe, “A Hybrid Deep Learning Approach for Replay and DDoS Attack Detection in a Smart City,” IEEE Access, vol. 9, pp. 154864–154875, 2021, doi: 10.1109/ACCESS.2021.3128701.
T. Saba, A. R. Khan, T. Sadad, and S. Hong, “Securing the IoT System of Smart City against Cyber Threats Using Deep Learning,” Discrete Dynamics in Nature and Society, vol. 2022, no. 1, p. 1241122, Jan. 2022, doi: 10.1155/2022/1241122.
N. Kumar Agrawal et al., “TFL-IHOA: Three-Layer Federated Learning-Based Intelligent Hybrid Optimization Algorithm for Internet of Vehicle,” IEEE Transactions on Consumer Electronics, vol. 70, no. 3, pp. 5818–5828, Aug. 2024, doi: 10.1109/TCE.2023.3344129.
R. Vinayakumar, M. Alazab, S. Srinivasan, Q.-V. Pham, S. K. Padannayil, and K. Simran, “A Visualized Botnet Detection System Based Deep Learning for the Internet of Things Networks of Smart Cities,” IEEE Transactions on Industry Applications, vol. 56, no. 4, pp. 4436–4456, Jul. 2020, doi: 10.1109/TIA.2020.2971952.