Article Sidebar

Submitted
24 May 2025
Accepted
6 July 2025
Published
30 June 2025
Download
PDF
Statistic
Read Counter : 37

Main Article Content

Abstract

Internet of Things (IoT) technology is experiencing rapid development and increasing use in a variety of applications, making it a potential target for cyber-attacks. Machine learning and deep neural network techniques are an effective way to address these challenges and improve IoT security. This research aims to design a deep learning techniques for intrusion detection in an Internet of Things environment with limited resources. The research focuses on improving the efficiency and effectiveness of current model using artificial intelligence and LSTM algorithms, ensuring reliable and effective security in the IoT environment. The proposed model is evaluated using a realistic data set, Canadian Institute for Cybersecurity Internet of Things 2023 Dataset (CICIoT2023) devices, and using performance metrics, namely Accuracy, Precision, F1 Score, and Recall. The results show its compatibility and effectiveness in a real environment, with 99.1% accuracy recorded. This paper is considered an important contribution to the field of IoT security and provides an effective methodology for developing advanced security solutions in the IoT environment that enhance traffic analysis, identify abnormal behavior, and take the necessary measures. 


 


 

Keywords

Deep learning CICIoT2023 dataset IoT balancing methods

Article Details

License

Copyright (c) 2025 Noor Thamer, Suhad Hatem Jihad, Sumar Mohamed Khaleel, Ahmed Saleem Abbas (Author)

Creative Commons License

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

How to Cite
[1]
N. Thamer, Suhad Hatem Jihad, Sumar Mohamed Khaleel, and Ahmed Saleem Abbas, “Leveraging deep Learning for Efficient Intrusion Detection in IoT Networks”, Cybersys. J, vol. 2, no. 1, pp. 53–64, Jun. 2025, doi: 10.57238/csj.2025.1006.
Download Citation
Endnote/Zotero/Mendeley (RIS)
BibTeX

How to Cite

[1]
N. Thamer, Suhad Hatem Jihad, Sumar Mohamed Khaleel, and Ahmed Saleem Abbas, “Leveraging deep Learning for Efficient Intrusion Detection in IoT Networks”, Cybersys. J, vol. 2, no. 1, pp. 53–64, Jun. 2025, doi: 10.57238/csj.2025.1006.

References

  1. W. Kassab and K. A. Darabkh, “A–Z survey of Internet of Things: Architectures, protocols, applications, recent advances, future directions and recommendations,” J. Netw. Comput. Appl., vol. 163, p. 102663, Aug. 2020, doi: 10.1016/j.jnca.2020.102663.
  2. M. Alaa, A. A. Zaidan, B. B. Zaidan, M. Talal, and M. L. M. Kiah, “A review of smart home applications based on Internet of Things,” J. Netw. Comput. Appl., vol. 97, pp. 48–65, Nov. 2017, doi: 10.1016/j.jnca.2017.08.017.
  3. P. Guo, K. Xiao, X. Wang, and D. Li, “Multi-source heterogeneous data access management framework and key technologies for electric power Internet of Things,” Global Energy Interconnection, 2024, doi: 10.1016/j.gloei.2024.01.009.
  4. X. Liang and Y. Kim, “A survey on security attacks and solutions in the IoT network,” in Proc. IEEE 11th Annu. Comput. Commun. Workshop Conf. (CCWC), Jan. 2021, pp. 853–859, doi: 10.1109/CCWC51732.2021.9376174.
  5. M. A. Ferrag, L. Maglaras, A. Argyriou, D. Kosmanos, and H. Janicke, “Security for 4G and 5G cellular networks: A survey of existing authentication and privacy-preserving schemes,” J. Netw. Comput. Appl., vol. 101, pp. 55–82, Jan. 2018, doi: 10.1016/j.jnca.2017.10.017.
  6. X. Yang et al., “Towards a low-cost remote memory attestation for the smart grid,” Sensors, vol. 15, no. 8, pp. 20799–20824, Aug. 2015, doi: 10.3390/s150820799.
  7. J. Lin, W. Yu, and X. Yang, “Towards multistep electricity prices in smart grid electricity markets,” IEEE Trans. Parallel Distrib. Syst., vol. 27, no. 1, pp. 286–302, Jan. 2016, doi: 10.1109/TPDS.2015.2388479.
  8. G. Gomez, F. J. Lopez-Martinez, D. Morales-Jimenez, and M. R. McKay, “On the equivalence between interference and eavesdropping in wireless communications,” IEEE Trans. Veh. Technol., vol. 64, no. 12, pp. 5935–5940, Dec. 2015, doi: 10.1109/TVT.2014.2387475.
  9. N. Zhao, F. R. Yu, M. Li, and V. C. M. Leung, “Anti-eavesdropping schemes for interference alignment (IA)-based wireless networks,” IEEE Trans. Wireless Commun., vol. 15, no. 8, pp. 5719–5732, Aug. 2016, doi: 10.1109/TWC.2016.2568188.
  10. K. Zhang, X. Liang, R. Lu, and X. Shen, “Sybil attacks and their defenses in the Internet of Things,” IEEE Internet Things J., vol. 1, no. 5, pp. 372–383, Oct. 2014, doi: 10.1109/JIOT.2014.2344013.
  11. K. N. Qureshi, S. S. Rana, A. Ahmed, and G. Jeon, “A novel and secure attacks detection framework for smart cities industrial Internet of Things,” Sustain. Cities Soc., vol. 61, p. 102343, Oct. 2020, doi: 10.1016/j.scs.2020.102343.
  12. A. Mayzaud, R. Badonnel, and I. Chrisment, “A taxonomy of attacks in RPL-based Internet of Things,” Int. J. Netw. Secur., vol. 18, no. 3, pp. 459–473, 2016, doi: 10.13140/RG.2.2.23255.62885.
  13. I. Butun, P. Osterberg, and H. Song, “Security of the Internet of Things: Vulnerabilities, attacks, and countermeasures,” IEEE Commun. Surveys Tuts., vol. 22, no. 1, pp. 616–644, Jan. 2020, doi: 10.1109/COMST.2019.2953364.
  14. M. El-Hajj, A. Fadlallah, M. Chamoun, and A. Serhrouchni, “A survey of Internet of Things (IoT) authentication schemes,” Sensors, vol. 19, no. 5, art. no. 1141, Mar. 2019, doi: 10.3390/s19051141.
  15. M. Frustaci, P. Pace, G. Aloi, and G. Fortino, “Evaluating critical security issues of the IoT world: Present and future challenges,” IEEE Internet Things J., vol. 5, no. 4, pp. 2483–2495, Aug. 2018, doi: 10.1109/JIOT.2017.2767291.
  16. ITU-T, “Y.2066: Common requirements of the Internet of Things,” ITU-T Recommendation Y.2066, Jun. 22, 2014, doi: 11.1002/1000/12169.
  17. J. Asharf et al., “A review of intrusion detection systems using machine and deep learning in Internet of Things: Challenges, solutions and future directions,” Electronics, vol. 9, no. 7, art. no. 1177, Jul. 2020, doi: 10.3390/electronics9071177.
  18. ITU-T, “Y.4101: Common requirements and capabilities of a gateway for Internet of Things applications,” ITU-T Recommendation Y.4101/Y.2067, Oct. 29, 2017, doi: 11.1002/1000/13384.
  19. ENISA, “Baseline security recommendations for Internet of Things in the context of critical information infrastructures,” ENISA Rep., Nov. 2017. [Online]. Available: https://data.europa.eu/doi/10.2824/03228
  20. A. Odeh and A. Abu Taleb, “Ensemble-based deep learning models for enhancing IoT intrusion detection,” Appl. Sci., vol. 13, no. 21, art. no. 11985, Nov. 2023, doi: 10.3390/app132111985.
  21. S. Latif et al., “Intrusion detection framework for the Internet of Things using a dense random neural network,” IEEE Trans. Ind. Informat., vol. 18, no. 9, pp. 6435–6444, Sep. 2022, doi: 10.1109/TII.2021.3130248.
  22. A. Li and S. Yi, “Intelligent intrusion detection method of industrial Internet of Things based on CNN-BiLSTM,” Secur. Commun. Netw., vol. 2022, art. no. 5448647, 2022, doi: 10.1155/2022/5448647.
  23. S. Hanif, T. Ilyas, and M. Zeeshan, “Intrusion detection in IoT using artificial neural networks on UNSW-15 dataset,” in Proc. IEEE 16th Int. Conf. Smart Cities: Improving Quality Life Using ICT, IoT and A.I. (HONET-ICT), Charlotte, NC, USA, Oct. 2019, pp. 152–156, doi: 10.1109/HONET.2019.8908122.
  24. N. Thereza and K. Ramli, “Development of intrusion detection models for IoT networks utilizing CICIoT2023 dataset,” in Proc. 3rd Int. Conf. Smart Cities, Autom. Intell. Comput. Syst. (ICON-SONICS), Bali, Indonesia, 2023, pp. 66–72, doi: 10.1109/ICON-SONICS59898.2023.10435006.
  25. V. Ravi, R. Chaganti, and M. Alazab, “Deep learning feature fusion approach for an intrusion detection system in SDN-based IoT networks,” IEEE Internet Things Mag., vol. 5, no. 2, pp. 24–29, 2022, doi: 10.1109/IOTM.003.2200001.
  26. Z. Wang et al., “A lightweight intrusion detection method for IoT based on deep learning and dynamic quantization,” PeerJ Comput. Sci., vol. 9, art. no. e1569, 2023, doi: 10.7717/peerj-cs.1569.
  27. R. Chaganti, V. Ravi, and T. D. Pham, “Deep learning based cross architecture Internet of Things malware detection and classification,” Comput. Secur., vol. 120, art. no. 102779, 2022, doi: 10.1016/j.cose.2022.102779.
  28. E. C. P. Neto et al., “CICIoT2023: A real-time dataset and benchmark for large-scale attacks in IoT environment,” Sensors, vol. 23, no. 13, art. no. 5941, Jul. 2023, doi: 10.3390/s23135941.
  29. B. Nemade, V. Bharadi, S. S. Alegavi, and B. Marakarkandy, “A comprehensive review: SMOTE-based oversampling methods for imbalanced classification techniques, evaluation, and result comparisons,” Int. J. Intell. Syst. Appl. Eng., vol. 11, no. 9S, pp. 790–803, Jul. 2023. [Online]. Available: https://ijisae.org/index.php/IJISAE/article/view/3268
  30. L. Huang et al., “Normalization techniques in training DNNs: Methodology, analysis and application,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 45, no. 8, pp. 10173–10196, Aug. 2023, doi: 10.1109/TPAMI.2023.3250241.
  31. R. Alkhatib, W. Sahwan, A. Alkhatieb, and B. Schütt, “A brief review of machine learning algorithms in forest fires science,” Appl. Sci., vol. 13, no. 14, art. no. 8275, Jul. 2023, doi: 10.3390/app13148275.
  32. A. I. Jony and A. K. B. Arnob, “A long short-term memory based approach for detecting cyber attacks in IoT using CIC-IoT2023 dataset,” J. Edge Comput., 2024, doi: 10.55056/jec.648.