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Original Article
A Situation Based Predictive Approach for Cybersecurity Intrusion Detection and Prevention Using Machine Learning and Deep Learning Algorithms in Wireless Sensor Networks of Industy4.0
Abubakar Sithik M1
Md Arshad2
Medhavi Praveen Shenvi3
Mehak Siddique4
Nidhi H P5
1 Professor Department of Computer Science and Engineering, RajaRajeswari College of Engineering, Bangalore, Karnataka, India. 2 3 4 5 Department of Computer Science and Engineering, RajaRajeswari College of Engineering, Bangalore, Karnataka, India.
Published Online: November-December 2025
Pages: 144-149
Cite this article
↗ https://www.doi.org/10.59256/ijsreat.20250506022
References
1. M Ghobakhloo, “Industry 4.0, digitization, and opportunities for sustainability,” Journal of Cleaner Production, vol. 252, Art. no. 119869, Apr. 2020, doi: 10.1016/j.jclepro.2019.119869.
2. W. Zhang, D. Han, K.-C. Li, and F. I. Massetto, “Wireless sensor intrusion detection system based on MK- ELM,” Soft Computing, vol, 24, no.16, pp. 12361–12374, Aug. 2020, doi: 10.1007/s00500-020-04678-1.
3. S. Kumar and R. R. Mallipeddi, “Impact of cybersecurity on operations and supply chain management: Emerging trends and future research directions,” Production and Operations Management, vol. 31, no. 12, pp. 4488–4500, Dec. 2022.
4. [4]N. Farnaaz and M. A. Jabbar, “Random forest modeling for network intrusion detection system,” Procedia Computer Science, vol. 89, pp. 213–217, Jan. 2016
5. [5] Y. Song, S. Hyun, and Y.-G. Cheong, “Analysis of autoencoders for network intrusion detection,” Sensors, vol. 21, no. 13, p. 4294, Jun. 2021, doi: 10.3390/s21134294.
6. A. R. Javed, M. Usman, S. Rehman, H. U. Khan, and M. A. Alqarni, “Secure and energy-efficient intrusion detection system for wireless sensor networks using deep learning,” IEEE Transactions on Industrial Informatics, vol. 18, no. 8, pp. 5404–5412, Aug. 2022, doi: 10.1109/TII.2021.3138759.
7. I. Almomani, B. Al-Kasasbeh, and M. Al-Akhras, “WSN- DS: A dataset for network intrusion monitoring tools in wireless sensor networks,” Journal of Sensors, vol. 2016, pp. 1–16, Jan. 2016, doi: 10.1155/2016/4731953.
8. J. Kim, M. Park, H. Kim, S. Cho, and P. Kang, "Insider threat detection based on user behavior modeling and anomaly detection algorithms," Applied Sciences, vol. 9, no. 19, p. 4018, Sep. 2019, doi: 10.3390/app9194018.
9. ISO/IEC 27001, Information security management systems, International Organization for Standardization, 2013.
10. National Institute of Standards and Technology (NIST), “Framework for Improving Critical Infrastructure Cybersecurity,” Version 1.1, Apr. 2018.
11. IEC 62443, “Industrial communication networks – Network and system security,” International Electrotechnical Commission, 2018.
2. W. Zhang, D. Han, K.-C. Li, and F. I. Massetto, “Wireless sensor intrusion detection system based on MK- ELM,” Soft Computing, vol, 24, no.16, pp. 12361–12374, Aug. 2020, doi: 10.1007/s00500-020-04678-1.
3. S. Kumar and R. R. Mallipeddi, “Impact of cybersecurity on operations and supply chain management: Emerging trends and future research directions,” Production and Operations Management, vol. 31, no. 12, pp. 4488–4500, Dec. 2022.
4. [4]N. Farnaaz and M. A. Jabbar, “Random forest modeling for network intrusion detection system,” Procedia Computer Science, vol. 89, pp. 213–217, Jan. 2016
5. [5] Y. Song, S. Hyun, and Y.-G. Cheong, “Analysis of autoencoders for network intrusion detection,” Sensors, vol. 21, no. 13, p. 4294, Jun. 2021, doi: 10.3390/s21134294.
6. A. R. Javed, M. Usman, S. Rehman, H. U. Khan, and M. A. Alqarni, “Secure and energy-efficient intrusion detection system for wireless sensor networks using deep learning,” IEEE Transactions on Industrial Informatics, vol. 18, no. 8, pp. 5404–5412, Aug. 2022, doi: 10.1109/TII.2021.3138759.
7. I. Almomani, B. Al-Kasasbeh, and M. Al-Akhras, “WSN- DS: A dataset for network intrusion monitoring tools in wireless sensor networks,” Journal of Sensors, vol. 2016, pp. 1–16, Jan. 2016, doi: 10.1155/2016/4731953.
8. J. Kim, M. Park, H. Kim, S. Cho, and P. Kang, "Insider threat detection based on user behavior modeling and anomaly detection algorithms," Applied Sciences, vol. 9, no. 19, p. 4018, Sep. 2019, doi: 10.3390/app9194018.
9. ISO/IEC 27001, Information security management systems, International Organization for Standardization, 2013.
10. National Institute of Standards and Technology (NIST), “Framework for Improving Critical Infrastructure Cybersecurity,” Version 1.1, Apr. 2018.
11. IEC 62443, “Industrial communication networks – Network and system security,” International Electrotechnical Commission, 2018.
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