Comparative Analysis of Machine Learning Models for Intrusion Detection in Internet of Things Networks Using the RT-IoT2022 Dataset
DOI:
https://doi.org/10.57152/malcom.v4i2.1304Keywords:
Cyber Threat Detection, Internet of Things (IoT) Security, Intrusion Detection Systems (IDS), Machine Learning Models, Network Traffic ClassificationAbstract
This research investigates the performance of various machine learning models in developing an Intrusion Detection System (IDS) for the complex and evolving security landscape of Internet of Things (IoT) networks. Employing the RT-IoT2022 dataset, which captures a diverse array of IoT devices and attack methodologies, we meticulously evaluated four prominent models: Gradient Boosting, Random Forest, Logistic Regression, and Multi-Layer Perceptron (MLP). Our results indicate that both Gradient Boosting and Random Forest achieved perfect scores with an accuracy, precision, recall, and F1 score of 1.00, suggesting their superior ability to classify and predict security incidents within the dataset. Logistic Regression demonstrated commendable consistency with scores of 0.96 across all metrics, proposing a balance between model complexity and performance. The MLP model closely followed, with an accuracy, precision, recall, and F1 score of 0.99, highlighting its potential in capturing complex, nonlinear data relationships. These findings underscore the critical role of machine learning in fortifying IoT networks against cyber threats and the need for continuous model evaluation against real-world data. The study provides a pathway for future research to refine these IDS models for operational efficiency and sustainability in the dynamic IoT security domain.
References
D. Lupton, “The internet of things: social dimensions,” Sociol. Compass, vol. 14, no. 4, p. e12770, 2020.
O. Vermesan and P. Friess, Digitising the Industry Internet of Things Connecting the Physical, Digital and VirtualWorlds. CRC Press, 2022.
S. Nižeti?, P. Šoli?, D. L.-I. Gonzalez-De, L. Patrono, and others, “Internet of Things (IoT): Opportunities, issues and challenges towards a smart and sustainable future,” J. Clean. Prod., vol. 274, p. 122877, 2020.
J. Smith and C. Liu, “Secure Transactions, Secure Systems: Regulatory Compliance in Internet Banking,” 2024.
L. Kasowaki and K. Ali, “Cyber Hygiene: Safeguarding Your Data in a Connected World,” 2024.
S. Ahmed and M. Khan, “Securing the Internet of Things (IoT): A comprehensive study on the intersection of cybersecurity, privacy, and connectivity in the IoT ecosystem,” AI, IoT Fourth Ind. Revolut. Rev., vol. 13, no. 9, pp. 1–17, 2023.
P. Malhotra, Y. Singh, P. Anand, D. K. Bangotra, P. K. Singh, and W.-C. Hong, “Internet of things: Evolution, concerns and security challenges,” Sensors, vol. 21, no. 5, p. 1809, 2021.
M. Z. Gunduz and R. Das, “Cyber-security on smart grid: Threats and potential solutions,” Comput. networks, vol. 169, p. 107094, 2020.
A. E. Omolara et al., “The internet of things security: A survey encompassing unexplored areas and new insights,” Comput. & Secur., vol. 112, p. 102494, 2022.
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, 2023.
M. Schmitt, “Securing the Digital World: Protecting smart infrastructures and digital industries with Artificial Intelligence (AI)-enabled malware and intrusion detection,” J. Ind. Inf. Integr., vol. 36, p. 100520, 2023.
A. Odeh and A. Abu Taleb, “Ensemble-Based Deep Learning Models for Enhancing IoT Intrusion Detection,” Appl. Sci., vol. 13, no. 21, p. 11985, 2023.
A. Gilad and A. Tishler, “Mitigating the Risk of Advanced Cyber Attacks: The Role of Quality, Covertness and Intensity of Use of Cyber Weapons,” Def. Peace Econ., pp. 1–21, 2023.
S. Xu, “The cybersecurity dynamics way of thinking and landscape,” in Proceedings of the 7th ACM Workshop on Moving Target Defense, 2020, pp. 69–80.
G. Kong, F. Chen, X. Yang, G. Cheng, S. Zhang, and W. He, “Optimal Deception Asset Deployment in Cybersecurity: A Nash Q-Learning Approach in Multi-Agent Stochastic Games,” Appl. Sci., vol. 14, no. 1, p. 357, 2023.
M. A. I. Mallick and R. Nath, “Navigating the Cyber security Landscape: A Comprehensive Review of Cyber-Attacks, Emerging Trends, and Recent Developments.”
A. J. Hintaw, S. Manickam, M. F. Aboalmaaly, and S. Karuppayah, “MQTT vulnerabilities, attack vectors and solutions in the internet of things (IoT),” IETE J. Res., vol. 69, no. 6, pp. 3368–3397, 2023.
A. S. Dina and D. Manivannan, “Intrusion detection based on machine learning techniques in computer networks,” Internet of Things, vol. 16, p. 100462, 2021.
E. Gyamfi and A. Jurcut, “Intrusion detection in internet of things systems: a review on design approaches leveraging multi-access edge computing, machine learning, and datasets,” Sensors, vol. 22, no. 10, p. 3744, 2022.
A. Aldweesh, A. Derhab, and A. Z. Emam, “Deep learning approaches for anomaly-based intrusion detection systems: A survey, taxonomy, and open issues,” Knowledge-Based Syst., vol. 189, p. 105124, 2020.
G. D. L. T. Parra, P. Rad, K.-K. R. Choo, and N. Beebe, “Detecting Internet of Things attacks using distributed deep learning,” J. Netw. Comput. Appl., vol. 163, p. 102662, 2020.
J. Asharf, N. Moustafa, H. Khurshid, E. Debie, W. Haider, and A. Wahab, “A review of intrusion detection systems using machine and deep learning in internet of things: Challenges, solutions and future directions,” Electronics, vol. 9, no. 7, p. 1177, 2020.
A. Aldhaheri, F. Alwahedi, M. A. Ferrag, and A. Battah, “Deep learning for cyber threat detection in IoT networks: A review,” Internet Things Cyber-Physical Syst., 2023.
A. Djenna, S. Harous, and D. E. Saidouni, “Internet of things meet internet of threats: New concern cyber security issues of critical cyber infrastructure,” Appl. Sci., vol. 11, no. 10, p. 4580, 2021.
D. Kumar, “A principled approach to measuring the IoT ecosystem,” 2020.
P. Mishra and G. Singh, “Energy management systems in sustainable smart cities based on the internet of energy: A technical review,” Energies, vol. 16, no. 19, p. 6903, 2023.
F. Bouchama and M. Kamal, “Enhancing Cyber Threat Detection through Machine Learning-Based Behavioral Modeling of Network Traffic Patterns,” Int. J. Bus. Intell. Big Data Anal., vol. 4, no. 9, pp. 1–9, 2021.
D. Dechouniotis, N. Athanasopoulos, A. Leivadeas, N. Mitton, R. Jungers, and S. Papavassiliou, “Edge computing resource allocation for dynamic networks: The DRUID-NET vision and perspective,” Sensors, vol. 20, no. 8, p. 2191, 2020.
Y. Ma, S. Chen, S. Ermon, and D. B. Lobell, “Transfer learning in environmental remote sensing,” Remote Sens. Environ., vol. 301, p. 113924, 2024.
J. B. Capital, “Real-Time Internet of Things (RT-IoT2022).” 2022.
