IoT Network Security based on Intrusion Detection System using Stacked Ensemble

8268a686-dd7e-497c-8e70-08e9d4599be820250625063528362wseas:wseasmdt@crossref.orgMDT DepositWSEAS TRANSACTIONS ON INFORMATION SCIENCE AND APPLICATIONS2224-34021790-083210.37394/23209http://wseas.org/wseas/cms.action?id=40461720251720252210.37394/23209.2025.22https://wseas.com/journals/isa/2025.phpIoT Network Security based on Intrusion Detection System using Stacked EnsembleHassanKhazaneRITM Laboratory, CED Engineering Sciences, ENSEM Hassan 2 University Casablanca MOROCCOMohammedRidouaniRITM Laboratory, CED Engineering Sciences, ENSEM Hassan 2 University Casablanca MOROCCOFatimaSalahdineDept. of Electrical and Computer Engineering University of North Carolina Charlotte, North Carolina USANaimaKaabouchArtificial Intelligence Research Center University of North Dakota Grand Forks, North Dakota USAThe rapid evolution of IoT networks has led to an increasing number of devices connecting to the internet, exposing them to various cyber threats. Detecting intrusions in IoT environments is essential but challenging. Network Intrusion Detection Systems are vital in analyzing network traffic to differentiate normal and malicious activities without compromising security. However, the abundance of benign traffic complicates accurate detection. To overcome this challenge, we propose an Ensemble-based Network Intrusion Detection Systems framework, where five Machine Learning classifiers are combined through a Stacking approach and with nature-inspired feature selection techniques to enhance the detection effectiveness. The performance of the proposed model was compared to four base models - Random Forest, Extra Trees, AdaBoost, and Gradient Boosting - in terms of several metrics. The experimental results on the CICIoT2023 dataset reveal that the proposed stacking model consistently outperforms the base classifiers across all evaluation metrics.6252025625202546647338https://wseas.com/journals/isa/2025/a745109-017(2025).pdf10.37394/23209.2025.22.38https://wseas.com/journals/isa/2025/a745109-017(2025).pdf10.3390/fi16010032H. Khazane, M. Ridouani, F. Salahdine, and N. Kaabouch, “A Holistic Review of Machine Learning Adversarial Attacks in IoT Networks,” Future Internet, vol. 16, no. 1, p. 32, Jan. 2024, doi: 10.3390/fi16010032. 10.37394/232018.2024.12.27N. R. Al-Milli and Y. A. Al-Khassawneh, “Intrusion Detection System using CNNs and GANs,” WSEAS Transactions on Computer Research, vol. 12, pp. 281–290, Apr. 2024, https://doi.org/10.37394/232018.2024.12.27. T. J. Lucas, I. S. de Figueiredo, C. A. C. Tojeiro, A. M. G. de Almeida, R. Scherer, J. R. F. Brega, J. P. Papa, K. . P. da Costa, “A Comprehensive Survey on Ensemble Learning-Based Intrusion Detection Approaches in Computer Networks,” IEEE Access, vol. 11, pp. 122638–122676, 2023, doi: 10.1109/ACCESS.2023.3328535. D. H. Wolpert, “Stacked generalization,” Neural Networks, vol. 5, no. 2, pp. 241–259, Jan. 1992, doi: 10.1016/S0893- 6080(05)80023-1. 10.1155/2020/4586875S. Rajagopal, P. P. Kundapur, and K. S. Hareesha, “A Stacking Ensemble for Network Intrusion Detection Using Heterogeneous Datasets,” Security and Communication Networks, vol. 2020, pp. 1–9, Jan. 2020, doi: 10.1155/2020/4586875. S. Roy, J. Li, B.-J. Choi, and Y. Bai, “A lightweight supervised intrusion detection mechanism for IoT networks,” Future Generation Computer Systems, vol. 127, pp. 276–285, Feb. 2022, doi: 10.1016/j.future.2021.09.027. 10.1016/j.compeleceng.2021.107039S. I. Popoola, B. Adebisi, M. Hammoudeh, H. Gacanin, and G. Gui, “Stacked recurrent neural network for botnet detection in smart homes,” Computers & Electrical Engineering, vol. 92, p. 107039, Jun. 2021, doi: 10.1016/j.compeleceng.2021.107039. 10.1186/s13635-019-0098-yJ. Sharma, C. Giri, O.-C. Granmo, and M. Goodwin, “Multi-layer intrusion detection system with ExtraTrees feature selection, extreme learning machine ensemble, and softmax aggregation,” EURASIP J. on Info. Security, vol. 2019, no. 1, Dec. 2019, doi: 10.1186/s13635-019-0098-y. 10.1007/s12652-020-02696-3P. Kumar, G. P. Gupta, and R. Tripathi, “A distributed ensemble design based intrusion detection system using fog computing to protect the internet of things networks,” J Ambient Intell Human Comput, vol. 12, no. 10, pp. 9555–9572, Oct. 2021, doi: 10.1007/s12652-020-02696-3. 10.1016/j.knosys.2023.110941R. Lazzarini, H. Tianfield, and V. Charissis, “A stacking ensemble of deep learning models for IoT intrusion detection,” Knowledge-Based Systems, vol. 279, p. 110941, Nov. 2023, doi: 10.1016/j.knosys.2023.110941. 10.3390/s23135941E. C. P. Neto, S. Dadkhah, R. Ferreira, A. Zohourian, R. Lu, and A. A. Ghorbani, “CICIoT2023: A Real-Time Dataset and Benchmark for Large-Scale Attacks in IoT Environment,” Sensors, vol. 23, no. 13, p. 5941, Jun. 2023, doi: 10.3390/s23135941.