Exploration of Real-Time Anomaly Detection in Industrial IoT Using ML

Authors

  • Mr. Ashwani Kumar ,Dr. Bipin Pandey

Keywords:

Anomaly Detection, Security, IoT, Support Vector Machine (SVM), Random Forest (RF), Gray Wolf Optimizer (GWO), Particle Swarm Optimizer (PSO).

Abstract

This research focuses on optimizing the length of independent feature sets for MachineLearning algorithms to detect anomalies in real-time in IoT networks. The researcherspropose an intelligently initialized hybrid binary Particle Swarm Optimizer

References

Alazzam, H., Sharieh, A. and Sabri, K.E., 2020. A feature selection algo rithm for intrusion detection

system based on pigeon inspired optimizer. Expert systems with applications, 148, p.113249.

Aljarah, I., Mafarja, M., Heidari, A.A., Faris, H. and Mirjalili, S., 2019. Clustering analysis using a

Novel locality-informed grey wolf-inspired clustering approach. KNowledge and Information Systems,

pp.1-33.

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Published

2024-01-23

How to Cite

Mr. Ashwani Kumar ,Dr. Bipin Pandey. (2024). Exploration of Real-Time Anomaly Detection in Industrial IoT Using ML . Journal of Computational Analysis and Applications (JoCAAA), 32(1), 723–731. Retrieved from https://www.eudoxuspress.com/index.php/pub/article/view/2980

Issue

Vol. 32 No. 1 (2024): JOCAAA

Section

Articles

License

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.