Research Article
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Year 2025, Volume: 9 Issue: 2, 385 - 393, 30.06.2025

Abhay Singh

https://doi.org/10.31127/tuje.1516046

Abstract

References

  • King, J., & Awad, A. I. (2016). A distributed security mechanism for resource-constrained IoT devices. Informatica, 40(1), 133-143.
  • Weber, M., & Boban, M. (2016). Security challenges of the internet of things. In 2016 39th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), 638–643. https://10.1109/MIPRO.2016.7522219
  • Dirik, M. (2023). Machine learning-based lung cancer diagnosis. Turkish Journal of Engineering, 7(4), 322–330. https://doi.org/10.31127/tuje.1180931
  • Cao, K., Liu, Y., Meng, G., & Sun, Q. (2020). An overview on edge computing research. IEEE Access, 8, 85714–85728. https:// 10.1109/ACCESS.2020.2991734
  • Liu, B., Luo, Z., Chen, H., & Li, C. (2022). A survey of state-of-the-art on edge computing: Theoretical models, technologies, directions, and development paths. IEEE Access, 10, 54038–54063. https://10.1109/ACCESS.2022.3176106
  • Bakhsh, S. A., Khan, M. A., Ahmed, F., Alshehri, M. S., Ali, H., & Ahmad, J. (2023). Enhancing IoT network security through deep learning-powered intrusion detection system. Internet of Things, 24, 100936. https://doi.org/10.1016/j.iot.2023.100936
  • Ghazal, T. M., Hasan, M. K., Alzoubi, H. M., Alshurideh, M., Ahmad, M., & Akbar, S. S. (2023). Internet of Things connected wireless sensor networks for smart cities. In The effect of information technology on business and marketing intelligence systems, 1056, 1953–1968, Cham: Springer International Publishing. https://doi.org/10.1007/978-3-031-12382-5_107
  • Neto, E. C. P., Dadkhah, S., Ferreira, R., Zohourian, A., Lu, R., & Ghorbani, A. A. (2023). CICIoT2023: A real-time dataset and benchmark for large-scale attacks in IoT environment. Sensors, 23(13), 5941. https://doi.org/10.3390/s23135941
  • Falayi, A., Wang, Q., Liao, W., & Yu, W. (2023). Survey of distributed and decentralized IoT securities: Approaches using deep learning and blockchain technology. Future Internet, 15(5), 178. https://doi.org/10.3390/fi15050178
  • Shao, S., Zheng, J., Guo, S., Qi, F., & Qiu, I. X. (2023). Decentralized AI-enabled trusted wireless network: A new collaborative computing paradigm for the Internet of Things. IEEE Network, 37(2), 54–61. https:// 10.1109/MNET.002.2200391
  • Song, F., Ma, Y., Yuan, Z., You, I., Pau, G., & Zhang, H. (2023). Exploring reliable decentralized networks with smart collaborative theory. IEEE Communications Magazine, 61(8), 44–50. https:// 10.1109/MCOM.003.2200443
  • Li, T., Yu, L., Ma, Y., et al. (2023). Carbon emissions of 5G mobile networks in China. Nature Sustainability, 6(12), 1620–1631. https://doi.org/10.1038/s41893-023-01206-5
  • Kao, H. W., & Wu, E. H. K. (2023). QoE sustainability on 5G and beyond 5G networks. IEEE Wireless Communications, 30(1), 118–125. https:// 10.1109/MWC.007.2200260
  • Gendreau, A. A., & Moorman, M. (2016). Survey of intrusion detection systems towards an end-to-end secure Internet of Things. In 2016 IEEE 4th International Conference on Future Internet of Things and Cloud (FiCloud), 84–90. https:// 10.1109/FiCloud.2016.20
  • Packet Total - A useful site for analyzing PCAP files. (n.d.). Bleeping Computer. Retrieved December 14, 2023, from https://www.bleepingcomputer.com/news/security/packettotal-a-useful-site-for-analyzing-pcap-files/
  • Wireshark. (n.d.). Retrieved December 7, 2023, from https://www.wireshark.org/
  • Banerjee, U., Vashishtha, A., & Saxena, M. (2010). Evaluation of the capabilities of Wireshark as a tool for intrusion detection. International Journal of Computer Applications, 6(7), 1–5.
  • Canadian Institute for Cybersecurity (CIC). (n.d.). Retrieved December 7, 2024, from https://www.unb.ca/cic/datasets/index.html
  • Loganathan, G., Samarabandu, J., & Wang, X. (2018). Real-time intrusion detection in network traffic using adaptive and auto-scaling stream processor. In 2018 IEEE Global Communications Conference (GLOBECOM), 1–6. https:// 10.1109/GLOCOM.2018.8647489
  • Rathore, M. M., Paul, A., Ahmad, A., Rho, S., Imran, M., & Guizani, M. (2016). Hadoop-based real-time intrusion detection for high-speed networks. In 2016 IEEE Global Communications Conference (GLOBECOM), 1–6. https:// 0.1109/GLOCOM.2016.7841864
  • Lee, S. J., Yoo, P. D., Asyhari, A. T., Jhi, Y., Chermak, L., Yeun, C. Y., & Taha, K. (2020). IMPACT: Impersonation attack detection via edge computing using deep autoencoder and feature abstraction. IEEE Access, 8, 65520–65529. https:// 10.1109/ACCESS.2020.2985089
  • Shaikh, A., & Gupta, P. (2022). Real-time intrusion detection based on residual learning through ResNet algorithm. International Journal of System Assurance Engineering and Management, 1–15. https://doi.org/10.1007/s13198-021-01558-1
  • Kaya, Y., Şenol, H. İ., Yiğit, A. Y., & Yakar, M. (2023). Car detection from very high-resolution UAV images using deep learning algorithms. Photogrammetric Engineering & Remote Sensing, 89(2), 117-123.
  • Singh, A. P., Singh, M., Bhatia, K., Pathak, H. (2024). Encrypted malware detection methodology without decryption using deep learning-based approaches. Turkish Journal of Engineering, 8(3), 498-509. https://doi.org/10.31127/tuje.1416933
  • Raju, V. G., Lakshmi, K. P., Jain, V. M., Kalidindi, A., & Padma, V. (2020). Study the influence of normalization/transformation process on the accuracy of supervised classification. In 2020 Third International Conference on Smart Systems and Inventive Technology (ICSSIT), 729–735. https:// 10.1109/ICSSIT48917.2020.9214160
  • Patro, S. G. O. P. A. L., & Sahu, K. K. (2015). Normalization: A preprocessing stage. arXiv. https://arxiv.org/abs/1503.06462
  • Maurya, A., & Gaur, S. (2023). A decision tree classifier-based ensemble approach to credit score classification. In 2023 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS), 620–624. https:// 10.1109/ICCCIS60361.2023.10425039
  • Khandelwal, N., & Sakalle, V. (2024). A review of customer churn prediction in telecommunications and the medical industry using machine learning classification models. International Journal of Innovative Research in Technology and Science, 12(2), 366–379.
  • Basholli, F.,Mema, B.,& Basholli, A. (2024). Training of information technology personnel through simulations for protection against cyber-attacks. Engineering Applications, 3(1), 45-58
  • 30. Leka, B., & Hoxha, K. (2024). Software engineering methodologies in programming companies in Albania. Engineering Applications, 3(1), 85-91
  • Pradhan, D., & Muduli, D. (2023). Software defect prediction model using AdaBoost-based random forest technique. In 2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT), 1–6. https:// 10.1109/ICCCNT56998.2023.10308208
  • Pradhan, D., & Muduli, D. (2023). Software defect prediction model using AdaBoost-based random forest technique. In 2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT), 1–6. https:// 10.1109/ICCCNT56998.2023.10308208
  • Mogaraju, J. K. (2024). Machine learning empowered prediction of geolocation using groundwater quality variables over YSR district of India. Turkish Journal of Engineering, 8(1), 31-45. https://doi.org/10.31127/tuje.1223779
  • Raman, R., Kantari, H., Gokhale, A. A., Elangovan, K., Meenakshi, B., & Srinivasan, S. (2024). Agriculture yield estimation using machine learning algorithms. In 2024 International Conference on Automation and Computation (AUTOCOM), 187–191. https:// 10.1109/AUTOCOM60220.2024.10486107
  • Juraev, D. A., Elsayed, E. E., Bulnes, J. J. D., Agarwal, P., & Saeed, R. K. (2023). History of ill-posed problems and their application to solve various mathematical problems. Engineering Applications, 2(3), 279–290. https://publish.mersin.edu.tr/index.php/enap/article/view/1178
  • Mema, B., & Basholli, F. (2023). Internet of Things in the development of future businesses in Albania. Advanced Engineering Science, 3, 196–205. https://publish.mersin.edu.tr/index.php/ades/article/view/1325
  • Demiröz, A., Barstugan, M. ., Saran, O., & Battal, H. (2023). Determination of compaction parameters by image analysis technique. Advanced Engineering Science, 3, 137–150. https://publish.mersin.edu.tr/index.php/ades/article/view/1192
  • Kocalar, A. C. (2023). Sinkholes caused by agricultural excess water using and administrative traces of the process. Advanced Engineering Science, 3, 15-20
  • Naumov, A., Khmarskiy, P., Byshnev, N., & Piatrouski, M. (2023). Methods and software for estimation of total electron content in ionosphere using GNSS observations. Engineering Applications, 2(3), 243–253. Retrieved September 14, 2024, from https://publish.mersin.edu.tr/index.php/enap/article/view/1165
  • Meghraoui, K., Sebari, I., Bensiali, S., & Ait El Kadi, K. (2022). On behalf of an intelligent approach based on 3D CNN and multimodal remote sensing data for precise crop yield estimation: Case study of wheat in Morocco. Advanced Engineering Science, 2, 118–126. Retrieved September 14, 2024, from https://publish.mersin.edu.tr/index.php/ades/article/view/329
  • Vishwakarma, M., & Kesswani, N. (2023). A new two-phase intrusion detection system with Naïve Bayes machine learning for data classification and elliptic envelope method for anomaly detection. Decision Analytics Journal, 7, 100233. https://doi.org/10.1016/j.dajour.2023.100233
  • Saini, N., Bhat Kasaragod, V., Prakasha, K., & Das, A. K. (2023). A hybrid ensemble machine learning model for detecting APT attacks based on network behavior anomaly detection. Concurrency and Computation: Practice and Experience, 35(28), e7865. https://doi.org/10.1002/cpe.7865

Real Time Intrusion Detection In Edge Computing Using Machine Learning Techniques

Year 2025, Volume: 9 Issue: 2, 385 - 393, 30.06.2025

Abhay Singh

https://doi.org/10.31127/tuje.1516046

Abstract

The proliferation of edge computing has introduced new opportunities for optimizing latency-sensitive and bandwidth-intensive applications by processing the data closer to its source. In addition, this paradigm shift also brings forth unique security challenges, particularly in the realm of intrusion detection. In edge computing environments, where data is processed at the network edge closer to the data source, real-time intrusion detection is crucial to safeguard the security of the system. The attackers are also exploiting the edge network with rapid extension. Conversely, conventional Intrusion Detection Systems (IDS) cannot detect the latest types of attack patterns in high-speed real-time networks due to their complex behavior and low processing capability. This study introduces a novel approach for developing an effective IDS model to handle such threats in a real-time network and explores the design and implementation of a real-time intrusion detection system (IDS) tailored for edge computing environments. The proposed model is found to be methodical and reliable, and employs supervised Machine Learning (ML) techniques. The objective is to precisely recognize and categorize harmful intrusions or malignant activities within the network in real-time. In order to train and test the model, a self created dataset which utilizes both malevolent and benign PCAPs (Packet Capture files) is used in this research study. To determine the usefulness of the IDS model, the random forest, decision tree, extra tree, and K-nearest neighbors were used as classification techniques. The proposed IDS model exhibhits excellent performance based on several factors such as adaptability and scalability. The model also generates higher values for accuracy, detection rate, F-measure, precision, recall, and lower FPR.

Keywords

IOT devices Edge Computing IDS Machine learning

Thanks

Respected sir, As per you suggestions, I have done all the necessarily changes in the manuscript. Thanks for kind cooperation.

References

  • King, J., & Awad, A. I. (2016). A distributed security mechanism for resource-constrained IoT devices. Informatica, 40(1), 133-143.
  • Weber, M., & Boban, M. (2016). Security challenges of the internet of things. In 2016 39th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), 638–643. https://10.1109/MIPRO.2016.7522219
  • Dirik, M. (2023). Machine learning-based lung cancer diagnosis. Turkish Journal of Engineering, 7(4), 322–330. https://doi.org/10.31127/tuje.1180931
  • Cao, K., Liu, Y., Meng, G., & Sun, Q. (2020). An overview on edge computing research. IEEE Access, 8, 85714–85728. https:// 10.1109/ACCESS.2020.2991734
  • Liu, B., Luo, Z., Chen, H., & Li, C. (2022). A survey of state-of-the-art on edge computing: Theoretical models, technologies, directions, and development paths. IEEE Access, 10, 54038–54063. https://10.1109/ACCESS.2022.3176106
  • Bakhsh, S. A., Khan, M. A., Ahmed, F., Alshehri, M. S., Ali, H., & Ahmad, J. (2023). Enhancing IoT network security through deep learning-powered intrusion detection system. Internet of Things, 24, 100936. https://doi.org/10.1016/j.iot.2023.100936
  • Ghazal, T. M., Hasan, M. K., Alzoubi, H. M., Alshurideh, M., Ahmad, M., & Akbar, S. S. (2023). Internet of Things connected wireless sensor networks for smart cities. In The effect of information technology on business and marketing intelligence systems, 1056, 1953–1968, Cham: Springer International Publishing. https://doi.org/10.1007/978-3-031-12382-5_107
  • Neto, E. C. P., Dadkhah, S., Ferreira, R., Zohourian, A., Lu, R., & Ghorbani, A. A. (2023). CICIoT2023: A real-time dataset and benchmark for large-scale attacks in IoT environment. Sensors, 23(13), 5941. https://doi.org/10.3390/s23135941
  • Falayi, A., Wang, Q., Liao, W., & Yu, W. (2023). Survey of distributed and decentralized IoT securities: Approaches using deep learning and blockchain technology. Future Internet, 15(5), 178. https://doi.org/10.3390/fi15050178
  • Shao, S., Zheng, J., Guo, S., Qi, F., & Qiu, I. X. (2023). Decentralized AI-enabled trusted wireless network: A new collaborative computing paradigm for the Internet of Things. IEEE Network, 37(2), 54–61. https:// 10.1109/MNET.002.2200391
  • Song, F., Ma, Y., Yuan, Z., You, I., Pau, G., & Zhang, H. (2023). Exploring reliable decentralized networks with smart collaborative theory. IEEE Communications Magazine, 61(8), 44–50. https:// 10.1109/MCOM.003.2200443
  • Li, T., Yu, L., Ma, Y., et al. (2023). Carbon emissions of 5G mobile networks in China. Nature Sustainability, 6(12), 1620–1631. https://doi.org/10.1038/s41893-023-01206-5
  • Kao, H. W., & Wu, E. H. K. (2023). QoE sustainability on 5G and beyond 5G networks. IEEE Wireless Communications, 30(1), 118–125. https:// 10.1109/MWC.007.2200260
  • Gendreau, A. A., & Moorman, M. (2016). Survey of intrusion detection systems towards an end-to-end secure Internet of Things. In 2016 IEEE 4th International Conference on Future Internet of Things and Cloud (FiCloud), 84–90. https:// 10.1109/FiCloud.2016.20
  • Packet Total - A useful site for analyzing PCAP files. (n.d.). Bleeping Computer. Retrieved December 14, 2023, from https://www.bleepingcomputer.com/news/security/packettotal-a-useful-site-for-analyzing-pcap-files/
  • Wireshark. (n.d.). Retrieved December 7, 2023, from https://www.wireshark.org/
  • Banerjee, U., Vashishtha, A., & Saxena, M. (2010). Evaluation of the capabilities of Wireshark as a tool for intrusion detection. International Journal of Computer Applications, 6(7), 1–5.
  • Canadian Institute for Cybersecurity (CIC). (n.d.). Retrieved December 7, 2024, from https://www.unb.ca/cic/datasets/index.html
  • Loganathan, G., Samarabandu, J., & Wang, X. (2018). Real-time intrusion detection in network traffic using adaptive and auto-scaling stream processor. In 2018 IEEE Global Communications Conference (GLOBECOM), 1–6. https:// 10.1109/GLOCOM.2018.8647489
  • Rathore, M. M., Paul, A., Ahmad, A., Rho, S., Imran, M., & Guizani, M. (2016). Hadoop-based real-time intrusion detection for high-speed networks. In 2016 IEEE Global Communications Conference (GLOBECOM), 1–6. https:// 0.1109/GLOCOM.2016.7841864
  • Lee, S. J., Yoo, P. D., Asyhari, A. T., Jhi, Y., Chermak, L., Yeun, C. Y., & Taha, K. (2020). IMPACT: Impersonation attack detection via edge computing using deep autoencoder and feature abstraction. IEEE Access, 8, 65520–65529. https:// 10.1109/ACCESS.2020.2985089
  • Shaikh, A., & Gupta, P. (2022). Real-time intrusion detection based on residual learning through ResNet algorithm. International Journal of System Assurance Engineering and Management, 1–15. https://doi.org/10.1007/s13198-021-01558-1
  • Kaya, Y., Şenol, H. İ., Yiğit, A. Y., & Yakar, M. (2023). Car detection from very high-resolution UAV images using deep learning algorithms. Photogrammetric Engineering & Remote Sensing, 89(2), 117-123.
  • Singh, A. P., Singh, M., Bhatia, K., Pathak, H. (2024). Encrypted malware detection methodology without decryption using deep learning-based approaches. Turkish Journal of Engineering, 8(3), 498-509. https://doi.org/10.31127/tuje.1416933
  • Raju, V. G., Lakshmi, K. P., Jain, V. M., Kalidindi, A., & Padma, V. (2020). Study the influence of normalization/transformation process on the accuracy of supervised classification. In 2020 Third International Conference on Smart Systems and Inventive Technology (ICSSIT), 729–735. https:// 10.1109/ICSSIT48917.2020.9214160
  • Patro, S. G. O. P. A. L., & Sahu, K. K. (2015). Normalization: A preprocessing stage. arXiv. https://arxiv.org/abs/1503.06462
  • Maurya, A., & Gaur, S. (2023). A decision tree classifier-based ensemble approach to credit score classification. In 2023 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS), 620–624. https:// 10.1109/ICCCIS60361.2023.10425039
  • Khandelwal, N., & Sakalle, V. (2024). A review of customer churn prediction in telecommunications and the medical industry using machine learning classification models. International Journal of Innovative Research in Technology and Science, 12(2), 366–379.
  • Basholli, F.,Mema, B.,& Basholli, A. (2024). Training of information technology personnel through simulations for protection against cyber-attacks. Engineering Applications, 3(1), 45-58
  • 30. Leka, B., & Hoxha, K. (2024). Software engineering methodologies in programming companies in Albania. Engineering Applications, 3(1), 85-91
  • Pradhan, D., & Muduli, D. (2023). Software defect prediction model using AdaBoost-based random forest technique. In 2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT), 1–6. https:// 10.1109/ICCCNT56998.2023.10308208
  • Pradhan, D., & Muduli, D. (2023). Software defect prediction model using AdaBoost-based random forest technique. In 2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT), 1–6. https:// 10.1109/ICCCNT56998.2023.10308208
  • Mogaraju, J. K. (2024). Machine learning empowered prediction of geolocation using groundwater quality variables over YSR district of India. Turkish Journal of Engineering, 8(1), 31-45. https://doi.org/10.31127/tuje.1223779
  • Raman, R., Kantari, H., Gokhale, A. A., Elangovan, K., Meenakshi, B., & Srinivasan, S. (2024). Agriculture yield estimation using machine learning algorithms. In 2024 International Conference on Automation and Computation (AUTOCOM), 187–191. https:// 10.1109/AUTOCOM60220.2024.10486107
  • Juraev, D. A., Elsayed, E. E., Bulnes, J. J. D., Agarwal, P., & Saeed, R. K. (2023). History of ill-posed problems and their application to solve various mathematical problems. Engineering Applications, 2(3), 279–290. https://publish.mersin.edu.tr/index.php/enap/article/view/1178
  • Mema, B., & Basholli, F. (2023). Internet of Things in the development of future businesses in Albania. Advanced Engineering Science, 3, 196–205. https://publish.mersin.edu.tr/index.php/ades/article/view/1325
  • Demiröz, A., Barstugan, M. ., Saran, O., & Battal, H. (2023). Determination of compaction parameters by image analysis technique. Advanced Engineering Science, 3, 137–150. https://publish.mersin.edu.tr/index.php/ades/article/view/1192
  • Kocalar, A. C. (2023). Sinkholes caused by agricultural excess water using and administrative traces of the process. Advanced Engineering Science, 3, 15-20
  • Naumov, A., Khmarskiy, P., Byshnev, N., & Piatrouski, M. (2023). Methods and software for estimation of total electron content in ionosphere using GNSS observations. Engineering Applications, 2(3), 243–253. Retrieved September 14, 2024, from https://publish.mersin.edu.tr/index.php/enap/article/view/1165
  • Meghraoui, K., Sebari, I., Bensiali, S., & Ait El Kadi, K. (2022). On behalf of an intelligent approach based on 3D CNN and multimodal remote sensing data for precise crop yield estimation: Case study of wheat in Morocco. Advanced Engineering Science, 2, 118–126. Retrieved September 14, 2024, from https://publish.mersin.edu.tr/index.php/ades/article/view/329
  • Vishwakarma, M., & Kesswani, N. (2023). A new two-phase intrusion detection system with Naïve Bayes machine learning for data classification and elliptic envelope method for anomaly detection. Decision Analytics Journal, 7, 100233. https://doi.org/10.1016/j.dajour.2023.100233
  • Saini, N., Bhat Kasaragod, V., Prakasha, K., & Das, A. K. (2023). A hybrid ensemble machine learning model for detecting APT attacks based on network behavior anomaly detection. Concurrency and Computation: Practice and Experience, 35(28), e7865. https://doi.org/10.1002/cpe.7865
There are 42 citations in total.

Details

Primary Language English
Subjects Information Security Management
Journal Section Articles
Authors

Abhay Singh 0000-0002-1044-7557

Early Pub Date January 20, 2025
Publication Date June 30, 2025
Submission Date July 14, 2024
Acceptance Date August 27, 2024
Published in Issue Year 2025 Volume: 9 Issue: 2

Cite

APA Singh, A. (2025). Real Time Intrusion Detection In Edge Computing Using Machine Learning Techniques. Turkish Journal of Engineering, 9(2), 385-393. https://doi.org/10.31127/tuje.1516046
AMA Singh A. Real Time Intrusion Detection In Edge Computing Using Machine Learning Techniques. TUJE. June 2025;9(2):385-393. doi:10.31127/tuje.1516046
Chicago Singh, Abhay. “Real Time Intrusion Detection In Edge Computing Using Machine Learning Techniques”. Turkish Journal of Engineering 9, no. 2 (June 2025): 385-93. https://doi.org/10.31127/tuje.1516046.
EndNote Singh A (June 1, 2025) Real Time Intrusion Detection In Edge Computing Using Machine Learning Techniques. Turkish Journal of Engineering 9 2 385–393.
IEEE A. Singh, “Real Time Intrusion Detection In Edge Computing Using Machine Learning Techniques”, TUJE, vol. 9, no. 2, pp. 385–393, 2025, doi: 10.31127/tuje.1516046.
ISNAD Singh, Abhay. “Real Time Intrusion Detection In Edge Computing Using Machine Learning Techniques”. Turkish Journal of Engineering 9/2 (June2025), 385-393. https://doi.org/10.31127/tuje.1516046.
JAMA Singh A. Real Time Intrusion Detection In Edge Computing Using Machine Learning Techniques. TUJE. 2025;9:385–393.
MLA Singh, Abhay. “Real Time Intrusion Detection In Edge Computing Using Machine Learning Techniques”. Turkish Journal of Engineering, vol. 9, no. 2, 2025, pp. 385-93, doi:10.31127/tuje.1516046.
Vancouver Singh A. Real Time Intrusion Detection In Edge Computing Using Machine Learning Techniques. TUJE. 2025;9(2):385-93.
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