Authors

Swapnil Deshpande

Department of Electronic Science, Savitribai Phule Pune University, Pune, Maharashtra, India

Abstract

The identification of anomalies in intelligent IoT sensor networks (SISN) is important to recognize atypical events or behaviors that can indicate security gaps or operational challenges. Conventional methods based on predefined rules are often inappropriate due to the complex and constantly developing nature of IoT ecosystems. On the other hand, automatic learning techniques (ML) have occurred as practical alternatives, with algorithms being used that learn data in order to identify themselves independently. In this journal article, a variety of ML strategies are examined that are used to detect anomalies in SISN that covers supervised learning approaches without supervision and semi-super-supervised. It deals with critical components such as data preparation, properties and selection of suitable algorithms to improve the accuracy and efficiency of recognition. Case studies are presented to demonstrate the use of ML techniques in IoT scenarios in the real world, which shows their effectiveness in the identification of different anomalies. In addition, the article examines evaluation measures for measuring the detection performance with an approach for measures such as precision, memory and F1 score. In summary, the article provides information on existing challenges, possible research routes and the potential influence of recognizing ML -anomalies to improve safety and reliability intelligent IoT -sensor networks.

Keywords

Machine Learning Identification of Anomalies Sensor Networks SISN ML AI IoT ecosystems Conventional methods

Citation of this Article

Swapnil Deshpande. (2025). Automated Machine Learning Methods for Identification of Anomalies in IoT Sensor Networks. Current Journal of Engineering and Science Research. 2(2), 6-11. Article DOI: https://doi.org/10.47001/CJESR/2025.202002

Licence Copyright (c) 2026 Current Journal of Engineering and Science Research. This work is licensed under a Creative Commons Attribution Non Commercial 4.0 International Licence.

References

  1. D. Ucci, L. Aniello, and R. Baldoni, “Survey of machine learning techniques for malware analysis,” Computers & Security, vol. 81, pp.123–147, 2019.
  2. S. M. Tahsien, H. Karimipour, and P. Spachos, “Machine learning based solutions for security of internet of things (iot): A survey,” Journal of Network and Computer Applications, vol. 161, p. 102630, 2020.
  3. F. Hussain, R. Hussain, S. A. Hassan, and E. Hossain, “Machine learning in iot security: Current solutions and future challenges,” IEEE Communications Surveys & Tutorials, vol. 22, no. 3, pp. 1686–1721, 2020.
  4. D. Gibert, C. Mateu, and J. Planes, “The rise of machine learning for detection and classification of malware: Research developments, trends and challenges,” Journal of Network and Computer Applications, vol.153, p. 102526, 2020.
  5. Haque, Ahshanul, et al. "Wireless sensor networks anomaly detection using machine learning: a survey." Intelligent Systems Conference. Cham: Springer Nature Switzerland, 2023.
  6. A.B. Nassif, M. A. Talib, Q. Nassir, H. Albadani, and F. D. Albab, “Ma-chine learning for cloud security: A systematic review,” IEEE Access, 2021.
  7. S. Yeom, I. Giacomelli, M. Fredrikson, and S. Jha, “Privacy risk inmachine learning: Analyzing the connection to overfitting,” in 2018 IEEE31st Computer Security Foundations Symposium (CSF). IEEE, 2018, pp.268–282.
  8. Bandyopadhyay, Debasis, and Jaydip Sen. "Internet of things: Applications and challenges in technology and standardization." Wireless personal communications 58 (2011): 49-69.
  9. Haji, Saad Hikmat, and Siddeeq Y. Ameen. "Attack and anomaly detection in iot networks using machine learning techniques: A review." Asian J. Res. Comput. Sci 9.2 (2021): 30-46.
  10. JONNERBY, JAKOB, A. BREZGER, and H. WANG. "Machine learning based novel architecture implementation for image processing mechanism." International Journal of communication and computer Technologies 11.1 (2023): 1-9.
  11. Cide, Felip, José Urebe, and Andrés Revera."Exploring Monopulse Feed Antennas for Low Earth Orbit Satellite Communication: Design, Advantages, and Applications." National Journal of Antennas and Propagation 4.2 (2022): 20-27.
  12. G. Sasikala, & G. Satya Krishna. (2023). Low Power Embedded SoC Design. Journal of VLSI Circuits and Systems, 6(1), 25–29. https://doi.org/10.31838/jvcs/06.01.04.
  13. Alghanmi, Nusaybah, Reem Alotaibi, and Seyed M. Buhari. "Machine learning approaches for anomaly detection in IoT: an overview and future research directions." Wireless Personal Communications 122.3 (2022): 2309-2324.
  14. Raghuvanshi, Ajay Singh, Rajeev Tripathi, and Sudarshan Tiwari. "Machine learning approach for anomaly detection in wireless sensor data." International Journal of Advances in Engineering & Technology 1.4 (2011): 47.
  15. K. A. da Costa, J. P. Papa, C. O. Lisboa, R. Munoz, and V. H. C.de Albuquerque, “Internet of things: A survey on machine learning-based intrusion detection approaches,” Computer Networks, vol. 151, pp. 147–157, 2019.
  16. Zhang, Hao, et al. "A network anomaly detection algorithm based on semi-supervised learning and adaptive multiclass balancing." The Journal of Supercomputing 79.18 (2023): 20445-20480.
  17. Chatterjee, Ayan, and Bestoun S. Ahmed. "IoT anomaly detection methods and applications: A survey." Internet of Things 19 (2022): 100568.
  18. Al-amri, Redhwan, et al. "A review of machine learning and deep learning techniques for anomaly detection in IoT data." Applied Sciences 11.12 (2021): 5320.