International Journal of Wireless and Microwave Technologies (IJWMT)
IJWMT Vol. 15, No. 2, 8 Apr. 2025
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Novel Machine Learning Approaches for Identifying Attacks in IoT-based Smart Home Environment
PDF (560KB), PP.41-50
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Author(s)
Oyelakin A. M.
1,*
Sanni S. A.
2
Adegbola I. A.
3
Salau-Ibrahim T. T.
4
Bakare-Busari Z. M.
1
Saka B. A.
1
Index Terms
Internet of Things, Attacks in IoT, Smart Homes, Meta Heuristic, Classification Model Performance
Abstract
Attackers keep launching different attacks on computer networks. Signature-based and Machine Learning (ML)-based techniques have been used to build models for promptly identifying these attacks in networks. However, ML-based approaches are more popular than their counterparts because of their ability to detect zero-day attacks. In the Internet of Things (IoT), devices are interconnected and this called for the need to guide such networks against intrusions. This study aims at building effective ML models from a recently released IoT-based Smart Home dataset. The study revealed patterns and characteristics of the IoT dataset, pre-processed it and then selected discriminant features using Binary Bat Algorithm (BBA). The pre-processing of the Smart Home IoT dataset for the study was carried out based on the issues identified during the exploratory analyses. The experimental evaluation carried out revealed that all the learning algorithms achieved promising classification results. For instance, Decision Trees recorded 98.60% accuracy, KNN produced 99.60% accuracy while Random Forest (RF) and AdaBoost-based models recorded 100.00% and 99.91% respectively. In all other metrics, RF-based attack classification model slightly recorded the best results. The study concluded that the EDA, innovative data pre-processing, BBA-based feature selection improved the classification performances of the ML approaches used in this study.
Cite This Paper
Oyelakin A. M., Sanni S. A., Adegbola I. A., Salau-Ibrahim T. T., Bakare-Busari Z. M., Saka B. A., "Novel Machine Learning Approaches for Identifying Attacks in IoT-based Smart Home Environment", International Journal of Wireless and Microwave Technologies(IJWMT), Vol.15, No.2, pp. 41-50, 2025. DOI:10.5815/ijwmt.2025.02.04
Reference
[1]O. Ibitoye, O. Shafiq, and A. Matrawy, “ Analyzing adversarial attacks against deep learning for intrusion detection in IoT networks.,” in 2019 IEEE global communications conference (GLOBECOM), 2019, pp. 1–6.
[2]Statista, “Annual number of Internet of Things (IoT) malware attacks worldwide from 2018 to 2022 (in millions),” 2023.
[3]A. Tasnim, N. Hossain, N. Parvin, S. Tabassum, R. Rahman, and M. I. Hossain, “Experimental analysis of classification for different internet of things (IoT) network attacks using machine learning and deep learning,” in In 2022 International Conference on Decision Aid Sciences and Applications (DASA) (pp. 406-410). IEEE., IEEE, Mar. 2022, pp. 406–410.
[4]J. Alsamiri and K. Alsubhi, “Internet of things cyber-attacks detection using machine learning.,” International Journal of Advanced Computer Science and Applications, vol. 10, no. 12, pp. 627–634, 2019.
[5]H. Touqeer, S. Zaman, R. Amin, M. Hussain, F. Al-Turjman, and M. Bilal, “Smart home security: challenges, issues and solutions at different IoT layers,” Journal of Supercomputing, vol. 77, no. 12, pp. 14053–14089, Dec. 2021, doi: 10.1007/S11227-021-03825-1/METRICS.
[6]A. Chakraborty , M. Islam, F. Shahriyar, S. Islam, H. U. Zaman, & M. Hassan (2023). “Smart home system: a comprehensive review”. Journal of Electrical and Computer Engineering, 2023(1), 7616683, 1-30, DOI:https://doi.org/10.1155/2023/7616683
[7]E. N. I. Bertino, “Botnets and internet of things security,” Computer (Long Beach Calif), vol. 50, no. 2, pp. 76–79, Feb. 2017.
[8]A. Scroxton, “Global cyber spend to rise 15% in 2025, pushed along by AI,” Computer Weekly, 2024.
[9]U. A. Adeniyi & A. M. Oyelakin, “A Survey on Promising Datasets and Recent Machine Learning Approaches for the Classification of Attacks in Internet of Things,” Journal of Information Technology and Computing, vol. 4, no. 2, pp. 31–38, 2023.
[10]J. O. Olomu, A. M. Oyelakin, O. M. Ayinla, & H. A. Ibrahim, “A Review on Attack Landscape and Machine Learning Techniques for the Classification of Attacks in Internet of Medical Things (IoMT),” LAUTECH Journal of Computing and Informatics (LAUJCI), vol. 4, no. 1, Apr. 2024.
[11]Khraisat A., V. P. Gondal, and Kamruzzaman J., “Survey of intrusion detection systems: techniques, datasets and challenges,” Cybersecurity, vol. 2, no. 1, 2019.
[12]Resende P.A.A. and Drummond A.C., “A survey of random forest based methods for intrusion detection systems,” ACM Comput. Surv, vol. 51, no. 3, pp. 1–36, 2018.
[13]S. Mirjalili, S. M. Mirjalili, and X.-S. Yang, “Binary bat algorithm,” Neural Comput Appl, vol. 25, no. 3–4, pp. 663–681, 2014.
[14]Milan Samantaray, Ram Chandra Barik, & Anil Kumar Biswal, “A comparative assessment of machine learning algorithms in the IoT-based network intrusion detection systems,” Decision Analytics, vol. 11, no. 100478, 2024.
[15]B. Xu, Lei Sun, Xiuqing Mao, Ruiyang Ding, and Chengwei Liu, “IoT Intrusion Detection System Based on Machine Learning,” Electronics (Basel), vol. 12, no. 20, p. 4289, 2023.
[16]Y. Majib , M. Alosaimi, A. Asaturyan & C. Perera (2023). Dataset for cyber–physical anomaly detection in smart homes. Frontiers in the Internet of Things, 2(1275080.), DOI: 10.3389/friot.2023.1275080
[17]A. A. Alsulami, Q. Abu Al-Haija, A. Tayeb, and A. Alqahtani, “An Intrusion Detection and Classification System for IoT Traffic with Improved Data Engineering. ,” Appl. Sci., vol. 12, no. 12336, 2022.
[18]S. Riaz, S. Latif, S. M. Usman, S. S. Ullah, A. D. Algarni, and A. Yasin, “Malware detection in internet of things (IoT) devices using deep learning,” Sensors, vol. 22, no. 23, p. 9305, 2022.
[19]F. Alghayadh and D. Debnath, “A Hybrid Intrusion Detection System for Smart Home Security Based on Machine Learning and User Behavior,” Advances in Internet of Things, vol. 11, pp. 10–25, 2021.
[20]Maxime Labonne, “Anomaly-based network intrusion detection using machine learning,” Institut Polytechnique de Paris, Paris, 2020.
[21]A. M. Oyelakin, A. O. Ameen, T. S. Ogundele , T. T. Salau-Ibrahim., U. T. Abdulrauf, H. I. Olufadi H.I,…, & Adeniji I. A., “Overview and Exploratory Analyses of CICIDS 2017 Intrusion Detection Dataset,” ournal of Systems Engineering and Information Technology (JOSEIT), vol. 2, no. 2, pp. 45–52, 2023.
[22]Jacob, “Smart Home Intrusion Detection Dataset.”, retrieved from https://www.kaggle.com/datasets/bobaaayoung/dataset-invade on 23rd September,2024
[23]Back T., Evolutionary algorithms in theory and practice. Oxford Univ.Press, 1996.
[24]I. Fister, X. S. Yang, S. Fong, and Y. Zhuang, “Bat algorithm: Recent advances,” in 2014 IEEE 15th International symposium on computational intelligence and informatics (CINTI), Nov. 2014, pp. 163–166.
[25]X. Yang, “A New Metaheuristic Bat-Insspired Algorithm. Nature Inspired Cooperative Strategies for Optimization.,” Studies in Computational Intelligence, pp. 65-74., 2010.
[26]R. Y. Nakamura, L. A. Pereira, K. A. Costa, D. Rodrigues, J. P. Papa, and X. S. Yang, “BBA: A Binary Bat Algorithm for Feature Selection.,” in BBA: a binary bat algorithm for feature selection. In 2012 25th SIBGRAPI conference on graphics, patterns and images, IEEE, 2012, pp. 291–297.
[27]A. M. Oyelakin & R. G. Jimoh “A survey of feature extraction and feature selection techniques used in machine learning-based botnet detection schemes.,” VAWKUM Transactions on Computer Sciences, vol. 9, no. 1, pp. 1–7, Sep. 2021.
[28]L. Breiman, “Random forests. ,” Mach Learn, vol. 45, pp. 5-32., 2001.
[29]Y. Freund, “Boosting a weak learning algorithm by majority,” Inf Comput, vol. 121, no. 2, pp. 256–285, 1995.