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- Vol. 5 Issue 3
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Containerized Anomaly Detection Framework for IoT Networks Using Machine Learning
Authors
1 Professor, Department of Information Science and Engineering, Bapuji Institute of Engineering, Karnataka, India. 2 3 4 5UG Scholars, Department of Information Science and Engineering, Bapuji Institute of Engineering, Karnataka, India.
Article Information
- Published In
- Volume 5, Issue 3 (2026)
- Published Online
- September-December 2026
- Pages
- 448-454
- Article Type
- Original Article
References
1. E. Krzysztoń, I. Rojek, and D. Mikołajewski, “A Comparative Analysis of Anomaly Detection Methods in IoT Networks: An
Experimental Study,” Applied Sciences, vol. 14, no. 24, Art. no. 11545, 2024.
2. M. Sinclair, “Machine Learning-Based Anomaly Detection in Industrial IoT (IIoT) Systems,” Australian Journal of Modern Research
& Applications, vol. 7, no. 7, 2024.
3. E. Omol, L. Mburu, and D. Onyango, “Anomaly Detection in IoT Sensor Data Using Machine Learning Techniques for Predictive
Maintenance in Smart Grids,” International Journal of Science, Technology & Management, 2024.
4. V. Prakash, O. Odedina, A. Kumar, L. Garg, and S. Bawa, “A Secure Framework for the Internet of Things Anomalies Using
Machine Learning,” Discover Internet of Things, Springer, 2024.
5. V. Mahavaishnavi, R. Saminathan, and R. Prithviraj, “Container Security Intelligence: Leveraging Machine Learning for Anomaly
Detection in Containerized Applications,” Journal of Propulsion Technology, 2023.
6. M. M. Khan and M. Alkhathami, “Anomaly Detection in IoT-Based Healthcare: Machine Learning for Enhanced Security,”
Scientific Reports, vol. 14, 2024.7. M. Balega et al., “Enhancing IoT Security: Optimizing Anomaly Detection through Machine Learning,” Electronics, vol. 13, no. 11,
2024.
8. N. Abbasi, M. Soltanaghaei, and F. Z. Boroujeni, “Anomaly Detection in IoT Edge Computing Using Deep Learning,” The Journal
of Supercomputing, 2024.
9. A. G. Ayad, N. A. Sakr, and N. A. Hikal, “A Hybrid Approach for Efficient Feature Selection in Anomaly Intrusion Detection for
IoT Networks,” The Journal of Supercomputing, 2024.
10. “Intrusion Detection Systems in IoT Based on Machine Learning: A State of the Art,” Procedia Computer Science, vol. 251, 2024.
11. K. K. Ponniah and B. Retnaswamy, “A Novel Deep Learning-Based Intrusion Detection System for the IoT-Cloud Platform,” Journal
of Information Security and Applications, 2023.
12. S. Yaras and M. Dener, “IoT-Based Intrusion Detection System Using New Hybrid Deep Learning Algorithm,” Electronics, 2024.
13. E. Krzysztoń et al., “A Comparative Analysis of Anomaly Detection Methods in IoT Networks,” Applied Sciences, 2024.
14. “A Survey of AI-Based Anomaly Detection in IoT and Sensor Networks,” Sensors, vol. 23, 2023.
15. S. Sharma et al., “Machine Learning-Based Intrusion Detection System for IoMT,” International Journal of System Assurance
Engineering and Management, 2024.
Experimental Study,” Applied Sciences, vol. 14, no. 24, Art. no. 11545, 2024.
2. M. Sinclair, “Machine Learning-Based Anomaly Detection in Industrial IoT (IIoT) Systems,” Australian Journal of Modern Research
& Applications, vol. 7, no. 7, 2024.
3. E. Omol, L. Mburu, and D. Onyango, “Anomaly Detection in IoT Sensor Data Using Machine Learning Techniques for Predictive
Maintenance in Smart Grids,” International Journal of Science, Technology & Management, 2024.
4. V. Prakash, O. Odedina, A. Kumar, L. Garg, and S. Bawa, “A Secure Framework for the Internet of Things Anomalies Using
Machine Learning,” Discover Internet of Things, Springer, 2024.
5. V. Mahavaishnavi, R. Saminathan, and R. Prithviraj, “Container Security Intelligence: Leveraging Machine Learning for Anomaly
Detection in Containerized Applications,” Journal of Propulsion Technology, 2023.
6. M. M. Khan and M. Alkhathami, “Anomaly Detection in IoT-Based Healthcare: Machine Learning for Enhanced Security,”
Scientific Reports, vol. 14, 2024.7. M. Balega et al., “Enhancing IoT Security: Optimizing Anomaly Detection through Machine Learning,” Electronics, vol. 13, no. 11,
2024.
8. N. Abbasi, M. Soltanaghaei, and F. Z. Boroujeni, “Anomaly Detection in IoT Edge Computing Using Deep Learning,” The Journal
of Supercomputing, 2024.
9. A. G. Ayad, N. A. Sakr, and N. A. Hikal, “A Hybrid Approach for Efficient Feature Selection in Anomaly Intrusion Detection for
IoT Networks,” The Journal of Supercomputing, 2024.
10. “Intrusion Detection Systems in IoT Based on Machine Learning: A State of the Art,” Procedia Computer Science, vol. 251, 2024.
11. K. K. Ponniah and B. Retnaswamy, “A Novel Deep Learning-Based Intrusion Detection System for the IoT-Cloud Platform,” Journal
of Information Security and Applications, 2023.
12. S. Yaras and M. Dener, “IoT-Based Intrusion Detection System Using New Hybrid Deep Learning Algorithm,” Electronics, 2024.
13. E. Krzysztoń et al., “A Comparative Analysis of Anomaly Detection Methods in IoT Networks,” Applied Sciences, 2024.
14. “A Survey of AI-Based Anomaly Detection in IoT and Sensor Networks,” Sensors, vol. 23, 2023.
15. S. Sharma et al., “Machine Learning-Based Intrusion Detection System for IoMT,” International Journal of System Assurance
Engineering and Management, 2024.
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