A Distributed Fog-Based Intrusion Detection Scheme for Industrial Internet of Things using Ensemble Approach
Keywords:
Distributed Denial of Service (DDoS), Ensemble technique, Industrial IoT, Fog Computing (FC), Intrusion detection, Machine Learning (ML)Abstract
The widespread growth of the Internet of Things (IoT) over the past few years has drastically transformed human life. Industrial IoT (IIoT) is a subset of the broader concept of IoT, which is predominantly deployed within the industrial environment. IIoT has revolutionized the industrial environment by automating business processes and enhancing production. However, this tremendous growth of IoT globally has greatly increased the threat of cyberattacks. Thus, there is a need to implement a lightweight, robust security system that can detect such attacks and take preventive measures. In this respect, the design of an intrusion detection scheme (IDS) gains importance in order to detect malicious activities and secure the data from misuse. In this work, a distributed intrusion detection scheme is proposed that involves the use of fog computing, dimensionality reduction, and an ensemble learning technique. The proposed model based on an ensemble learning algorithm is implemented on fog nodes, and the performance of the model has been analyzed using the Edge-IIoTset and Army Cyber Institute (ACI)-IoT2023 datasets. The results demonstrated the efficacy of the proposed model in terms of performance measures such as accuracy, detection rate, precision, and ROC (receiver operating characteristics).
References
H. Boyes, B. Hallaq, J. Cunningham, and T. Watson, “The industrial internet of things (IIoT): An analysis framework,” Computers in Industry, vol. 101, no. 1, pp. 1–12, Oct. 2018.
O. Mphale, K. N. Gorejena, and O. Nojila, “The future of things: a comprehensive overview of internet of things history, definitions, technologies, architectures, communication and beyond,” Journal of Information Systems and Informatics, vol. 6, no. 2, pp. 1263-1286, June 2024.
M. Mukherjee et al., “Security and privacy in fog computing: Challenges,” IEEE Access, vol. 5, pp. 19293-19304, 2017.
S. Anwar et al., “From intrusion detection to an intrusion response system: fundamentals, requirements, and future directions,” Algorithms, vol. 10, no. 2, p. 39, Mar. 2017.
A. Oun, K. Wince, and X. Cheng, “The role of artificial intelligence in boosting cybersecurity and trusted embedded systems performance: a systematic review on current and future trends,” IEEE Access, pp. 1-1, 2025.
K. Rajasekaran, “Classification and importance of intrusion detection system”, International Journal of Computer Science and Information Security, vol. 10, no. 8, 44, 2020.
I. Butun, S. D. Morgera, and R. Sankar, “A survey of intrusion detection systems in wireless sensor networks,” IEEE Communications Surveys & Tutorials, vol. 16, no. 1, pp. 266-282, 2014.
M. Zavvar et al., “A hybrid intrusion detection method based on multi-convolutional neural networks fusion and grey wolf optimizer,” Journal of Cloud Computing, vol. 15, no. 1, May 2026.
R. M. Czekster, T. Webber, L. B. Furstenau, and C. Marcon, “Dynamic risk assessment approach for analysing cyber security events in medical IoT networks,” Internet of Things, pp. 101437-101437, Nov. 2024.
Syed, M. H. Zafar, Mohamad Abou Houran, Z. Qadir, Syed, and F. Sanfilippo, “Enhancing cybersecurity in edge IIoT networks: An asynchronous federated learning approach with a deep hybrid detection model,” Internet of Things, vol. 27, pp. 101252-101252, June 2024.
A. Wells and A. B. Usman, “Privacy and biometrics for smart healthcare systems: attacks, and techniques,” Information Security Journal: A Global Perspective, vol. 33, no. 3, pp. 1-25, Oct. 2023.
H. C. Altunay and Z. Albayrak, “A hybrid CNN+LSTM-based intrusion detection system for industrial IoT networks,” Engineering Science and Technology, an International Journal, vol. 38, p. 101322, Feb. 2023.
S. Asha Varma and K. Ganesh Reddy, “An AI based IDS Framework for detecting DDoS attacks in cloud environment,” Information Security Journal: A Global Perspective, vol. 33, no. 6, pp. 613-625, Nov. 2023.
A. Abbas, M. A. Khan, S. Latif, M. Ajaz, A.A. Shah, J. Ahmad, “A new ensemble-based intrusion detection system for internet of things”, Arabian Journal for Science and Engineering, vol.47, pp. 1805-1819, (2022).
Y. Almutairi, B. Alhazmi, and A. Munshi, “Network intrusion detection using machine learning techniques,” Advances in Science and Technology Research Journal, vol. 16, no. 3, pp. 193-206, July 2022.
M. A. Lawal, R. A. Shaikh, and S. R. Hassan, “A DDoS attack mitigation framework for IoT networks using fog computing,” Procedia Computer Science, vol. 182, pp. 13-20, 2021.
X. Feng, Z. Xiao, B. Zhong, J. Qiu, and Y. Dong, “Dynamic ensemble classification for credit scoring using soft probability,” Applied Soft Computing, vol. 65, pp. 139-151, Apr. 2018.
X. Gao, C. Shan, C. Hu, Z. Niu, and Z. Liu, “An adaptive ensemble machine learning model for intrusion detection,” IEEE Access, vol. 7, pp. 82512-82521, 2019.
P. Kumar, G. P. Gupta, and R. Tripathi, “A distributed ensemble design-based intrusion detection system using fog computing to protect the Internet of Things networks,” Journal of Ambient Intelligence and Humanized Computing, vol. 12, no. 10, pp. 9555-9572, Nov. 2022.
M. A. Daneshwar, “Anomaly detection in data streams using machine learning and deep learning,” International Transactions on Electrical Engineering and Computer Science, vol. 3, no. 4, pp. 207-214, Jan. 2025.
Davide Ballabio, R. Todeschini, and V. Consonni, “Recent advances in high-level fusion methods to classify multiple analytical chemical data,” Data Handling in Science and Technology, pp. 129-155, Jan. 2019.
L. Breiman, “Bagging predictors,” Machine Learning, vol. 24, no. 2, pp. 123-140, 1996.
R. E. Schapire, “A brief introduction to boosting,” IJCAI, vol. 2, pp. 1401-1406, July 1999.
K.M. Ting, I.H. Witten, “Issues in stacked generalization”, Journal of Artificial Intelligence Research, vol.10, pp.271-289, 1999.
J. Li et al., “Feature selection: a data perspective,” ACM Computing Surveys, vol. 50, no. 6, pp. 1-45, Jan. 2018.
G. Guo, “A machine learning framework for intrusion detection system in IoT networks using an ensemble feature selection method,” 2021 IEEE 12th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON), pp. 0593-0599, Oct. 2021.
O. Almomani, “A feature selection model for network intrusion detection system based on PSO, GWO, FFA and GA algorithms,” Symmetry, vol. 12, no. 6, p. 1046, June 2020.
S. Abbas, M. Abu Talib, Q. Nasir, S. Idhis, M. Alaboudi, and A. Mohamed, “Radio frequency fingerprinting techniques for device identification: a survey,” International Journal of Information Security, vol. 23, no. 2, pp. 1389-1427, Dec. 2023.
S. I. Popoola, B. Adebisi, R. Ande, M. Hammoudeh, K. Anoh, and A. A. Atayero, “SMOTE-DRNN: A deep learning algorithm for botnet detection in the internet-of-things networks,” Sensors, vol. 21, no. 9, p. 2985, Apr. 2021.
M. A. Ferrag, O. Friha, D. Hamouda, L. Maglaras, and H. Janicke, “Edge-IIoTset: A new comprehensive realistic cyber security dataset of IoT and IIoT applications for centralized and federated learning,” IEEE Access, vol. 10, pp. 1-1, 2022.
N. Bastian, D. Bierbrauer, M. McKenzie, and E. Nack, “ACI IoT network traffic dataset 2023” 2023.
M. C. Belavagi and B. Muniyal, “Performance evaluation of supervised machine learning algorithms for intrusion detection,” Procedia Computer Science, vol. 89, pp. 117-123, 2016.
