Vol. 4 No. 2 (2025): April
RESEARCH ARTICLES

Leveraging Smart Sentry to Detect and Mitigate Cyber Threats in Industrial IoT Networks

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  • K Gayathri,
  • K Tulasi Kumar,
  • L Sai Vignesh,
  • P Ajay Prathap,
  • G Anusha
K Gayathri
Department of Computer Science and Engineering, Annamacharya Institute of Technology and Sciences, Kadapa, Andhra Pradesh, India
K Tulasi Kumar
Department of Computer Science and Engineering, Annamacharya Institute of Technology and Sciences, Kadapa, Andhra Pradesh, India
L Sai Vignesh
Department of Computer Science and Engineering, Annamacharya Institute of Technology and Sciences, Kadapa, Andhra Pradesh, India
P Ajay Prathap
Department of Computer Science and Engineering, Annamacharya Institute of Technology and Sciences, Kadapa, Andhra Pradesh, India
G Anusha
Department of Computer Science and Engineering, Annamacharya Institute of Technology and Sciences, Kadapa, Andhra Pradesh, India
DOI: https://doi.org/10.5281/zenodo.15163499

Published 2025-04-06

Keywords

  • Internet of Things,
  • Machine learning,
  • security,
  • Intrusion detection system

How to Cite

K Gayathri, K Tulasi Kumar, L Sai Vignesh, P Ajay Prathap, & G Anusha. (2025). Leveraging Smart Sentry to Detect and Mitigate Cyber Threats in Industrial IoT Networks. International Journal of Computational Learning & Intelligence, 4(2), 408–419. https://doi.org/10.5281/zenodo.15163499

Copyright (c) 2025 K Gayathri, K Tulasi Kumar, L Sai Vignesh, P Ajay Prathap, G Anusha

Creative Commons License

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

Abstract

The Internet of Things (IoT) has revolutionized digital connectivity but has simultaneously broadened the landscape for cyber threats. This paper focuses on categorizing IoT attacks by integrating multiple machine learning (ML) models and deep learning (DL) techniques. The research presents a binary and multiclass classification approach utilizing algorithms like Random Forest, Decision Tree, Extra Trees Classifier, Support Vector Machine, k-Nearest Neighbors, and a Deep Neural Network. Experiments were conducted using the Edge-IIoTset dataset, reflecting real-world IoT threat scenarios. Pre-processing involved Principal Component Analysis (PCA) for dimensionality reduction, Synthetic Minority Over-sampling Technique (SMOTE) to counteract class imbalance, and data normalization. The study provides a comparative analysis of model performance, showing that the DNN model achieved outstanding results—100% accuracy for binary classification, 96.15% for 6-class classification, and 94.68% for 15-class classification. A 10-fold cross-validation was also implemented to ensure model generalization. The findings offer valuable insights for improving the security posture of IoT environments through intelligent detection mechanisms.

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