Machine Learning Algorithms for Anomaly Detection in IoT Networks – A Review
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802-823
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Oct 26, 2024
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10.58578/amjsai.v1i2.4014
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Authors:
Muhammad Mamman Kontagora1, Bartholomew Idoko2 
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Abstract
Internet of Things (IoT) wide applications has significantly increased the need for robust anomaly detection to safeguard against countless security breaches. This paper presents a review that examines the effectiveness of hybrid solutions incorporating supervised and unsupervised machine learning models for enhancing IoT security. The review consolidates insights from a range of studies employing models such as Random Forest (RF), Support Vector Machine (SVM), k-nearest Neighbors (k-NN), and Gaussian Mixture Models (GMM). It integrates the findings of diverse research, emphasizing improvements in terms of detection accuracy and computational demands. The study delineates challenges in the field to evaluate the efficacy of hybrid techniques and their potential for immediate IoT security applications. Moreover, future research directions encompass the exploration of new algorithms and the integration of these approaches within dynamic IoT data streams.
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Kontagora, M. M., & Idoko, B. (2024). Machine Learning Algorithms for Anomaly Detection in IoT Networks – A Review. African Multidisciplinary Journal of Sciences and Artificial Intelligence, 1(2), 802-823. https://doi.org/10.58578/amjsai.v1i2.4014
Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
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25. A. Nabil, H. Harb, and I. F. T. Alshaikhli, "Anomaly detection in IoT networks using machine learning techniques: A comparative study," IEEE Access, vol. 8, pp. 54195-54207, 2020. doi: [10.1109/ACCESS.2020.2978363](https://doi.org/10.1109/ACCESS.2020.2978363).
26. Y. Zeng, H. Wang, and Z. Zhou, "Anomaly detection in IoT-based smart grids using boosting algorithms," IEEE Transactions on Industrial Informatics, vol. 16, no. 9, pp. 5774-5782, 2020. doi: [10.1109/TII.2019.2955831](https://doi.org/10.1109/TII.2019.2955831).
27. V. Mothukuri et al., "Federated learning-based anomaly detection for IoT security attacks," IEEE Internet of Things Journal, vol. 8, no. 8, pp. 6348-6358, 2021. doi: [10.1109/JIOT.2021.3063856](https://doi.org/10.1109/JIOT.2021.3063856).
28. Y. Liu et al., "Deep anomaly detection for time-series data in Industrial IoT: A communication-efficient on-device federated learning approach," IEEE Internet of Things Journal, vol. 8, no. 8, pp. 6348-6358, 2021. doi: [10.1109/JIOT.2020.3043756](https://doi.org/10.1109/JIOT.2020.3043756).
29. C. Wang, J. Chen, Y. Yang, X. Ma, and J. Liu, "Poisoning attacks and countermeasures in intelligent networks: Status quo and prospects," Digital Communications and Networks, 2021. doi: [10.1016/j.dcan.2021.10.003](https://doi.org/10.1016/j.dcan.2021.10.003).
30. H. Lee et al., "Digestive neural networks: A novel defense strategy against inference attacks in federated learning," Computers & Security, vol. 109, pp. 102378, 2021. doi: [10.1016/j.cose.2021.102378](https://doi.org/10.1016/j.cose.2021.102378).
31. C. Yin, S. Zhang, J. Wang, and N. N. Xiong, "Anomaly detection based on convolutional recurrent autoencoder for IoT time series," IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 50, no. 5, pp. 1719-1731, 2020. doi: [10.1109/TSMC.2020.2997340](https://doi.org/10.1109/TSMC.2020.2997340).
32. R. Shukla and S. Sengupta, "Scalable and robust outlier detector using hierarchical clustering and long short-term memory (LSTM) neural network for the Internet of Things," Internet of Things, vol. 9, pp. 100167, 2020. doi: [10.1016/j.iot.2020.100167](https://doi.org/10.1016/j.iot.2020.100167).
33. P. García-Teodoro, J. Díaz-Verdejo, G. Maciá-Fernández, and E. Vázquez, "Anomaly-based network intrusion detection: Techniques, systems and challenges," Computers & Security, vol. 28, no. 1-2, pp. 18-28, 2009. doi: [10.1016/j.cose.2008.08.003](https://doi.org/10.1016/j.cose.2008.08.003).
34. M. Sokolova and G. Lapalme, "A systematic analysis of performance measures for classification tasks," Information Processing & Management, vol. 45, no. 4, pp. 427-437, 2009. doi: [10.1016/j.ipm.2009.03.002](https://doi.org/10.1016/j.ipm.2009.03.002).
35. D. M. Powers, "Evaluation: From precision, recall and F-measure to ROC, informedness, markedness and correlation," Journal of Machine Learning Technologies, vol. 2, no. 1, pp. 37-63, 2011.
36. A. P. Bradley, "The use of the area under the ROC curve in the evaluation of machine learning algorithms," Pattern Recognition, vol. 30, no. 7, pp. 1145-1159, 1997. doi: [10.1016/S0031-3203(96)00142-2](https://doi.org/10.1016/S0031-3203(96)00142-2).
37. R. Kohavi, "A study of cross-validation and bootstrap for accuracy estimation and model selection," in International Joint Conference on Artificial Intelligence, vol. 14, pp. 1137-1143, 1995.
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