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Title: :  Enhanced Intrusion and Anomaly Detection in Industrial IoT Using Ensemble Learning and LSTM
PaperId: :  26388
Published in:   International Journal Of Advance Research And Innovative Ideas In Education
Publisher:   IJARIIE
e-ISSN:   2395-4396
Volume/Issue:    Volume 11 Issue 2 2025
DUI:    16.0415/IJARIIE-26388
Licence: :   IJARIIE is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Author NameAuthor Institute
A.Nithyavel KrishnaSri Venkatesa Perumal College Of Engineering And Technology
K.HemanthSri Venkatesa Perumal College Of Engineering And Technology
M.LokeshSri Venkatesa Perumal College Of Engineering And Technology
K.Harsha Vardhan ReddySri Venkatesa Perumal College Of Engineering And Technology
M.YugandharSri Venkatesa Perumal College Of Engineering And Technology
K.Sravan KumarSri Venkatesa Perumal College Of Engineering And Technology

Abstract

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

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IJARIIE 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 Volume 11 Issue 2 2025 Page 3080-3088
MLA 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 11.2(2025) : 3080-3088.
APA A.Nithyavel Krishna, K.Hemanth, M.Lokesh, K.Harsha Vardhan Reddy, M.Yugandhar, & K.Sravan Kumar. (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.
Chicago 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 11, no. 2 (2025) : 3080-3088.
Oxford 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, 2025, p. 3080-3088. Available from IJARIIE, https://ijariie.com/AdminUploadPdf/Enhanced_Intrusion_and_Anomaly_Detection_in_Industrial_IoT_Using_Ensemble_Learning_and_LSTM_ijariie26388.pdf (Accessed : ).
Harvard A.Nithyavel Krishna, K.Hemanth, M.Lokesh, K.Harsha Vardhan Reddy, M.Yugandhar, and K.Sravan Kumar. (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. 3080-3088IJARIIE [Online]. Available at: https://ijariie.com/AdminUploadPdf/Enhanced_Intrusion_and_Anomaly_Detection_in_Industrial_IoT_Using_Ensemble_Learning_and_LSTM_ijariie26388.pdf (Accessed : )
IEEE 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, Mar-App 2025. [Online]. Available: https://ijariie.com/AdminUploadPdf/Enhanced_Intrusion_and_Anomaly_Detection_in_Industrial_IoT_Using_Ensemble_Learning_and_LSTM_ijariie26388.pdf [Accessed : ].
Turabian 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 [Online]. volume 11 number 2 ().
Vancouver 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 [Internet]. 2025 [Cited : ]; 11(2) : 3080-3088. Available from: https://ijariie.com/AdminUploadPdf/Enhanced_Intrusion_and_Anomaly_Detection_in_Industrial_IoT_Using_Ensemble_Learning_and_LSTM_ijariie26388.pdf
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