Enhanced Intrusion and Anomaly Detection in Industrial IoT Using Ensemble Learning and LSTM

A.Nithyavel Krishna; K.Hemanth; M.Lokesh; K.Harsha Vardhan Reddy; M.Yugandhar; K.Sravan Kumar

Enhanced Intrusion and Anomaly Detection in Industrial IoT Using Ensemble Learning and LSTM

April 2025

Vol-11, Issue-2

Paper ID: 26388

ISSN: 2395-4396

Downloads: 0

Abstract & Details

Research Area

Elecf

Keywords

Industrial IoT anomaly detection machine learning LSTM ensemble learning cybersecurity real-time monitoring class imbalance CIC dataset predictive analytics

Abstract

The rapid growth of Industrial Internet of Things (IIoT) in manufacturing and production sectors has increased the need for intelligent, real-time anomaly detection systems to ensure operational reliability and cybersecurity. This paper proposes a machine learning-based anomaly detection framework tailored for industrial IoT environments, where timely identification of cyber threats and system faults is critical. Unlike traditional intrusion detection systems that depend on static rule-based engines or centralized infrastructure, the proposed system leverages a hybrid machine learning architecture combining Long Short-Term Memory (LSTM) networks and Ensemble Learning (EL) models. This hybrid approach enhances the system's ability to detect complex and minority-class anomalies with improved accuracy and lower false positives. Our solution utilizes real-time IIoT traffic data from the CIC IoT 2023 dataset, addressing class imbalance using advanced sampling techniques and ensuring high detection rates across varied attack types. The system is designed to be lightweight, scalable, and suitable for deployment in energy- and compute-constrained industrial environments. It provides a robust and intelligent solution to ensure safety, performance, and resilience in smart factory operations.

License

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Author Information

#	Name	Institute / Affiliation
1	A.Nithyavel Krishna	Sri Venkatesa Perumal College Of Engineering And Technology
2	K.Hemanth	Sri Venkatesa Perumal College Of Engineering And Technology
3	M.Lokesh	Sri Venkatesa Perumal College Of Engineering And Technology
4	K.Harsha Vardhan Reddy	Sri Venkatesa Perumal College Of Engineering And Technology
5	M.Yugandhar	Sri Venkatesa Perumal College Of Engineering And Technology
6	K.Sravan Kumar	Sri Venkatesa Perumal College Of Engineering And Technology

How to Cite

Use the following formats to cite this article in your research.

APA Style

Krishna, A.Nithyavel, K.Hemanth, M.Lokesh, Reddy, K.Harsha Vardhan, M.Yugandhar, & Kumar, K.Sravan (2025). Enhanced Intrusion and Anomaly Detection in Industrial IoT Using Ensemble Learning and LSTM. International Journal of Advance Research and Innovative Ideas In Education, 11(2), 3080-3088.

MLA Style

Krishna, A.Nithyavel, et al. "Enhanced Intrusion and Anomaly Detection in Industrial IoT Using Ensemble Learning and LSTM." International Journal of Advance Research and Innovative Ideas In Education, vol. 11, no. 2, 2025, pp. 3080-3088.

IEEE Style

A.Nithyavel Krishna, K.Hemanth, M.Lokesh, K.Harsha Vardhan Reddy, M.Yugandhar, and K.Sravan Kumar, "Enhanced Intrusion and Anomaly Detection in Industrial IoT Using Ensemble Learning and LSTM," International Journal of Advance Research and Innovative Ideas In Education, vol. 11, no. 2, pp. 3080-3088, 2025.

Vancouver Style

Krishna A.Nithyavel, K.Hemanth, M.Lokesh, Reddy K.Harsha Vardhan, M.Yugandhar, Kumar K.Sravan. Enhanced Intrusion and Anomaly Detection in Industrial IoT Using Ensemble Learning and LSTM. International Journal of Advance Research and Innovative Ideas In Education. 2025;11(2):3080-3088.

Harvard Style

Krishna, A.Nithyavel, K.Hemanth, M.Lokesh, Reddy, K.Harsha Vardhan, M.Yugandhar, & Kumar, K.Sravan (2025) 'Enhanced Intrusion and Anomaly Detection in Industrial IoT Using Ensemble Learning and LSTM', International Journal of Advance Research and Innovative Ideas In Education, 11(2), pp. 3080-3088.

Chicago Style

Krishna, A.Nithyavel, et al. "Enhanced Intrusion and Anomaly Detection in Industrial IoT Using Ensemble Learning and LSTM." International Journal of Advance Research and Innovative Ideas In Education 11, no. 2 (2025): 3080-3088.

Turabian Style

Krishna, A.Nithyavel, et al. "Enhanced Intrusion and Anomaly Detection in Industrial IoT Using Ensemble Learning and LSTM." International Journal of Advance Research and Innovative Ideas In Education 11, no. 2 (2025): 3080-3088.