Current - Issue
Year 2026 · Volume 6 · Issue 5
Review Article
Machine Learning and Deep Learning Approaches for Anomaly Detection: A Review
Shaifun Nahar Sonia1
Atul Sharma2
1 University of Science and Technology Chittagong (USTC), Chittagong, Bangladesh, South Asia. 2 Department of Computer Science and Engineering, University Institute of Engineering and Technology, Kurukshetra University, Kurukshetra, Haryana, India.
Published Online: September-October 2026
Pages: 349-354
Cite this article
↗ https://www.doi.org/10.59256/ijrtmr.20260605037
References
[1] Baimukhanov, S., Ali, H., & Yazici, A. (2025). Enhancing ML-based anomaly detection in data management for security through integration of IoT, cloud, and edge computing. Expert Systems with Applications, 293, 128700.
[2] Aslam, M. M., Tufail, A., De Silva, L. C., & Apong, R. A. A. H. M. (2025, August). Multi-Feature Hybrid Anomaly Detection in ICS: An Integration of ML, DL, and Statistical Techniques. In Proceedings of the 3rd ACM Workshop on Secure and Trustworthy Deep Learning Systems (pp. 43-51).
[3] Narmadha, S., & Balaji, N. V. (2025). Improved network anomaly detection system using optimized autoencoder− LSTM. Expert Systems with Applications, 273, 126854.
[4] Hernandez-Jaimes, M. L., Martinez-Cruz, A., Ramírez-Gutiérrez, K. A., & Morales-Reyes, A. (2025). Network traffic inspection to enhance anomaly detection in the Internet of Things using attention-driven Deep Learning. Integration, 103, 102398.
[5] Yadav, K. S., Kumbham, A. R., Vallurupalli, P., Mallreddy, S. R., & Mahendrakumar, G. (2025, August). A Deep Learning Framework for Network Anomaly Detection using KNN and LSTM Models. In 2025 3rd International Conference on Sustainable Computing and Data Communication Systems (ICSCDS) (pp. 2110-2115). IEEE.
[6] Mizanur, M., Kumer, S., & Reza, N. (2025). Machine learning-based anomaly detection for cyber threat prevention. Journal of Primeasia, 6(1), 1-8.
[7] Goumidi, H., & Pierre, S. (2025). Real-time anomaly detection in IoMT networks using stacking model and a healthcare-specific dataset. IEEE Access, 13, 70352-70365.
[8] Yuan, S., Li, J., Wang, C., & Zhang, X. (2026). Conformal machine learning for reliable anomaly detection in industrial cyber-physical systems. Reliability Engineering & System Safety, 112417.
[9] Ahmad, I., Yang, L., Alkhrijah, Y., Almadhor, A., Alawad, M. A., Peng, L., & Ho, P. H. (2026). Quantum machine learning for anomaly detection: The future of smarter and safer IoT networks. IEEE Network.
[10] Gawusu, S., Abu, M., & Mvile, B. N. (2026). Compositional data-aware deep learning for geochemical anomaly detection: A novel framework for mineral prospectivity mapping. Physics and Chemistry of the Earth, Parts A/B/C, 104352.
[11] Zhang, C., Zhu, H., Zhu, A., Liao, J., Xiao, Y., & Zhang, Z. (2026, February). Deep learning approach for protocol anomaly detection using status code sequences. In Proceedings of the 2026 International Conference on Generative Artificial Intelligence and Education (pp. 10-15).
[12] Li, Q., Ji, Y., Sun, L., Zhang, N., & Yang, T. (2026). Anomaly detection for industrial time series in process industry using informed machine learning with graph attention networks. Computers in Industry, 176, 104445.
[13] Wang, Z., Cao, J., & Di, X. (2026). Anomaly detection method for satellite networks based on genetic optimization federated learning. Expert Systems with Applications, 295, 128627.
[14] Abdelhady, G., Ghandoura, A., Motwakel, A., & Alajmi, A. (2026). Hybrid Machine Learning Anomaly Detection and Lightweight Zero Trust Authentication for LoRaWAN Networks. IEEE Access.
[15] Lachure, J., & Doriya, R. (2026). Intelligent sensor data analysis through hybrid deep hierarchical clustering for anomaly detection. Transactions of the Institute of Measurement and Control, 48(6), 1205-1220.
[2] Aslam, M. M., Tufail, A., De Silva, L. C., & Apong, R. A. A. H. M. (2025, August). Multi-Feature Hybrid Anomaly Detection in ICS: An Integration of ML, DL, and Statistical Techniques. In Proceedings of the 3rd ACM Workshop on Secure and Trustworthy Deep Learning Systems (pp. 43-51).
[3] Narmadha, S., & Balaji, N. V. (2025). Improved network anomaly detection system using optimized autoencoder− LSTM. Expert Systems with Applications, 273, 126854.
[4] Hernandez-Jaimes, M. L., Martinez-Cruz, A., Ramírez-Gutiérrez, K. A., & Morales-Reyes, A. (2025). Network traffic inspection to enhance anomaly detection in the Internet of Things using attention-driven Deep Learning. Integration, 103, 102398.
[5] Yadav, K. S., Kumbham, A. R., Vallurupalli, P., Mallreddy, S. R., & Mahendrakumar, G. (2025, August). A Deep Learning Framework for Network Anomaly Detection using KNN and LSTM Models. In 2025 3rd International Conference on Sustainable Computing and Data Communication Systems (ICSCDS) (pp. 2110-2115). IEEE.
[6] Mizanur, M., Kumer, S., & Reza, N. (2025). Machine learning-based anomaly detection for cyber threat prevention. Journal of Primeasia, 6(1), 1-8.
[7] Goumidi, H., & Pierre, S. (2025). Real-time anomaly detection in IoMT networks using stacking model and a healthcare-specific dataset. IEEE Access, 13, 70352-70365.
[8] Yuan, S., Li, J., Wang, C., & Zhang, X. (2026). Conformal machine learning for reliable anomaly detection in industrial cyber-physical systems. Reliability Engineering & System Safety, 112417.
[9] Ahmad, I., Yang, L., Alkhrijah, Y., Almadhor, A., Alawad, M. A., Peng, L., & Ho, P. H. (2026). Quantum machine learning for anomaly detection: The future of smarter and safer IoT networks. IEEE Network.
[10] Gawusu, S., Abu, M., & Mvile, B. N. (2026). Compositional data-aware deep learning for geochemical anomaly detection: A novel framework for mineral prospectivity mapping. Physics and Chemistry of the Earth, Parts A/B/C, 104352.
[11] Zhang, C., Zhu, H., Zhu, A., Liao, J., Xiao, Y., & Zhang, Z. (2026, February). Deep learning approach for protocol anomaly detection using status code sequences. In Proceedings of the 2026 International Conference on Generative Artificial Intelligence and Education (pp. 10-15).
[12] Li, Q., Ji, Y., Sun, L., Zhang, N., & Yang, T. (2026). Anomaly detection for industrial time series in process industry using informed machine learning with graph attention networks. Computers in Industry, 176, 104445.
[13] Wang, Z., Cao, J., & Di, X. (2026). Anomaly detection method for satellite networks based on genetic optimization federated learning. Expert Systems with Applications, 295, 128627.
[14] Abdelhady, G., Ghandoura, A., Motwakel, A., & Alajmi, A. (2026). Hybrid Machine Learning Anomaly Detection and Lightweight Zero Trust Authentication for LoRaWAN Networks. IEEE Access.
[15] Lachure, J., & Doriya, R. (2026). Intelligent sensor data analysis through hybrid deep hierarchical clustering for anomaly detection. Transactions of the Institute of Measurement and Control, 48(6), 1205-1220.
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