ARCHIVES
Original Article
An Intelligent AI Driven Frame work for Real Time Anomoly Detection in Internet of Things (IoT Systems)
Dr D.Kirubha1
Vinooj.S.K2
Dhanalakshmi.M3
Dhanushree.S4
Dhundubhi.S5
1 Professor & HoD, Department of CSE, Raja Rajeswari College of Engineering, Banagalore, Karnataka, India. 2 3 4 5 Department of CSE, Raja Rajeswari College of Engineering, Banagalore, Karnataka, India.
Published Online: March-April 2026
Pages: 476-483
Cite this article
↗ https://www.doi.org/10.59256/ijire.20260702057
References
1. E. Krzysztoń, I. Rojek, and D. Mikołajewski, “A Comparative Analysis of Anomaly Detection Methods in IoT Networks,” Applied Sciences, vol. 14, no. 24, p. 11545, 2024.
2. M. M. Khan and M. Alkhathami, “Anomaly Detection in IoT-Based Healthcare Using Machine Learning,” Scientific Reports, vol. 14, p. 5872, 2024.
3. M. Balega et al., “Enhancing IoT Security: Optimizing Anomaly Detection through Machine Learning,” Electronics, vol. 13, no. 11, p. 2148, 2024.
4. S. Trilles et al., “Anomaly Detection Based on Artificial Intelligence of Things: A Systematic Literature Mapping,” Internet of Things Journal, vol. 25, 2024.
5. L. Sana et al., “Enhancing Intrusion Anomaly Detection with Vision Transformers in IoT,” IEEE Access, vol. 12, pp. 82443–82468, 2024.
6. A. Aparcana-Tasayco, X. Deng, and J. H. Park, “A Systematic Review of Anomaly Detection in IoT Security: Towards Quantum Machine Learning Approach,” EPJ Quantum Technology, vol. 12, p. 112, 2025.
7. M. Z. Khan, A. Sabur, and H. Ghandorh, “A Novel IoMT Hybrid Model for Cybersecurity Anomaly Detection,” Sensors, vol. 25, no. 20, p. 6501, 2025.
8. L. Hazzam and S. Fenanir, “Anomaly Detection for IoT Using Machine Learning Techniques,” IJCESEN, vol. 11, no. 4, 2025.
9. A. Aparcana-Tasayco et al., “Quantum Machine Learning for IoT Anomaly Detection: A Review,” EPJ Quantum Technology, 2025.
10. Y. AlZahrani, “Real-Time Anomaly Detection in IoT Streams through Spatiotemporal Patterns,” Discover Internet of Things, vol. 6, 2026.
11. A. Benmachiche et al., “Real-Time Machine Learning for Embedded Anomaly Detection,” arXiv preprint, 2025.
12. E. Li et al., “CITADEL: Continual Anomaly Detection for IoT Intrusion Detection,” arXiv preprint, 2025.
13. T. Baranwal et al., “Machine Learning-Based Anomaly Detection of Correlated Sensor Data Using PCA-Autoencoder,” arXiv preprint, 2025.
14. M. S. A. Sami and M. Abid, “Unsupervised Anomaly Detection for Smart IoT Devices,” arXiv preprint, 2025.
15. “A Survey on Anomaly Detection in IoT: Techniques, Challenges, and 6G Integration,” Computer Networks, vol. 270, 2025.
2. M. M. Khan and M. Alkhathami, “Anomaly Detection in IoT-Based Healthcare Using Machine Learning,” Scientific Reports, vol. 14, p. 5872, 2024.
3. M. Balega et al., “Enhancing IoT Security: Optimizing Anomaly Detection through Machine Learning,” Electronics, vol. 13, no. 11, p. 2148, 2024.
4. S. Trilles et al., “Anomaly Detection Based on Artificial Intelligence of Things: A Systematic Literature Mapping,” Internet of Things Journal, vol. 25, 2024.
5. L. Sana et al., “Enhancing Intrusion Anomaly Detection with Vision Transformers in IoT,” IEEE Access, vol. 12, pp. 82443–82468, 2024.
6. A. Aparcana-Tasayco, X. Deng, and J. H. Park, “A Systematic Review of Anomaly Detection in IoT Security: Towards Quantum Machine Learning Approach,” EPJ Quantum Technology, vol. 12, p. 112, 2025.
7. M. Z. Khan, A. Sabur, and H. Ghandorh, “A Novel IoMT Hybrid Model for Cybersecurity Anomaly Detection,” Sensors, vol. 25, no. 20, p. 6501, 2025.
8. L. Hazzam and S. Fenanir, “Anomaly Detection for IoT Using Machine Learning Techniques,” IJCESEN, vol. 11, no. 4, 2025.
9. A. Aparcana-Tasayco et al., “Quantum Machine Learning for IoT Anomaly Detection: A Review,” EPJ Quantum Technology, 2025.
10. Y. AlZahrani, “Real-Time Anomaly Detection in IoT Streams through Spatiotemporal Patterns,” Discover Internet of Things, vol. 6, 2026.
11. A. Benmachiche et al., “Real-Time Machine Learning for Embedded Anomaly Detection,” arXiv preprint, 2025.
12. E. Li et al., “CITADEL: Continual Anomaly Detection for IoT Intrusion Detection,” arXiv preprint, 2025.
13. T. Baranwal et al., “Machine Learning-Based Anomaly Detection of Correlated Sensor Data Using PCA-Autoencoder,” arXiv preprint, 2025.
14. M. S. A. Sami and M. Abid, “Unsupervised Anomaly Detection for Smart IoT Devices,” arXiv preprint, 2025.
15. “A Survey on Anomaly Detection in IoT: Techniques, Challenges, and 6G Integration,” Computer Networks, vol. 270, 2025.
Related Articles
2026
AI-Based Stomach Cancer Detection Using Biomarkers, Medical Images, and Voice Analysis
2026
Hydrogen-Efficient Eco-Driving and Route Planning for Fuel-Cell Electric Vehicles Using Multi-Objective Optimization Under Traffic and Terrain Uncertainty
2026
A Data-Driven Machine Learning Framework for Assessing Patent Commercial Value and Technological Significance
2026
Evaluating Student Academic Performance Through a Benchmark of Fuzzy Reasoning Models
2026
A Hybrid Soft Computing Approach for Managing Uncertainty in Data Analytics
2026
