Enhancing IoT Network Security Through Deep Learning-Powered Intrusion Detection System

References

[1] “Internet of Things (IoT) Cybersecurity Research: A review of current research topics,” IEEE Journals & Magazine | IEEE Xplore, Apr. 01, 2019.

[2] Al-Fuqaha, M. Guizani, M. Mohammadi, M. Aledhari, and M. Ayyash, “Internet of Things: A survey on enabling technologies, protocols, and applications,” IEEE Communications Surveys & Tutorials, vol. 17, no. 4, pp. 2347–2376, Jan. 2015, doi: 10.1109/comst.2015.2444095.

[3] A. R. Khan, M. Kashif, R. H. Jhaveri, R. Raut, T. Saba, and S. A. Bahaj, “Deep Learning for Intrusion Detection and Security of Internet of Things (IoT): current analysis, challenges, and possible solutions,” Security and Communication Networks, vol. 2022, pp. 1–13, Jul. 2022, doi: 10.1155/2022/4016073.

[4] A. Rasheed, O. San, and T. Kvamsdal, “Digital Twin: values, challenges and enablers from a modeling perspective,” IEEE Access, vol. 8, pp. 21980–22012, Jan. 2020, doi: 10.1109/access.2020.2970143.

[5] C. Greer, M. Burns, D. Wollman, and E. Griffor, “Cyber-physical systems and internet of things,” Mar. 2019. doi: 10.6028/nist.sp.1900-202.

[6] C. Zhang, P. Patras, and H. Haddadi, “Deep learning in mobile and wireless Networking: a survey,” IEEE Communications Surveys & Tutorials, vol. 21, no. 3, pp. 2224–2287, Jan. 2019, doi: 10.1109/comst.2019.2904897.

[7] C. Zhang, P. Patras, and H. Haddadi, “Deep learning in mobile and wireless Networking: a survey,” IEEE Communications Surveys & Tutorials, vol. 21, no. 3, pp. 2224–2287, Jan. 2019, doi: 10.1109/comst.2019.2904897.

[8] C. Zhang, P. Patras, and H. Haddadi, “Deep learning in mobile and wireless Networking: a survey,” IEEE Communications Surveys & Tutorials, vol. 21, no. 3, pp. 2224–2287, Jan. 2019, doi: 10.1109/comst.2019.2904897.

[9] C. Zhou et al., “Design and analysis of Multimodel-Based anomaly intrusion detection Systems in industrial process automation,” IEEE Transactions on Systems Man and Cybernetics Systems, vol. 45, no. 10, pp. 1345–1360, Oct. 2015, doi: 10.1109/tsmc.2015.2415763.

[10] G. Kornaros, “Hardware-Assisted Machine Learning in Resource-Constrained IoT Environments for Security: review and future Prospective,” IEEE Access, vol. 10, pp. 58603–58622, Jan. 2022, doi: 10.1109/access.2022.3179047.

[11] GoogleScholar.”https://scholar.google.com/scholar_lookup?title=Industrial%20internet%20o f%20things%3A%20Challenges%2C%20opportunities%2C%20and%20directions&publication_year=2018&author=E.%20Sisinni&author=A.%20Saifullah&author=S.%20Han&author=U.%20Jennehag&author=M.%20Gidlund

[12] H. Teryak, A. Albaseer, M. Abdallah, S. Al-Kuwari, and M. Qaraqe, “Double-Edged Defense: Thwarting cyber-attacks and adversarial machine learning in IEC 60870-5-104 smart grids,” IEEE Open Journal of the Industrial Electronics Society, vol. 4, pp. 629–642, Jan. 2023, doi: 10.1109/ojies.2023.3336234.

[13] I. Ahmad et al., “Machine Learning Meets Communication Networks: Current trends and future challenges,” IEEE Access, vol. 8, pp. 223418–223460, Jan. 2020, doi: 10.1109/access.2020.3041765.

[14] I. Makhdoom, M. Abolhasan, J. Lipman, R. P. Liu, and W. Ni, “Anatomy of threats to the internet of things,” IEEE Communications Surveys & Tutorials, vol. 21, no. 2, pp. 1636–1675, Jan. 2019, doi: 10.1109/comst.2018.2874978.

[15] L. Alzubaidi et al., “A survey on deep learning tools dealing with data scarcity: definitions, challenges, solutions, tips, and applications,” Journal of Big Data, vol. 10, no. 1, Apr. 2023, doi: 10.1186/s40537-023-00727-2.

[16] M. A. Macneil et al., “Global status and conservation potential of reef sharks,” Nature, vol. 583, no. 7818, pp. 801–806, Jul. 2020, doi: 10.1038/s41586-020-2519-y.

[17] M. Stoyanova, Y. Nikoloudakis, S. Panagiotakis, E. Pallis, and E. K. Markakis, “A survey on the Internet of Things (IoT) forensics: Challenges, approaches, and open issues,” IEEE Communications Surveys & Tutorials, vol. 22, no. 2, pp. 1191–1221, Jan. 2020, doi: 10.1109/comst.2019.2962586.

[18] M. Stoyanova, Y. Nikoloudakis, S. Panagiotakis, E. Pallis, and E. K. Markakis, “A survey on the Internet of Things (IoT) forensics: Challenges, approaches, and open issues,” IEEE Communications Surveys & Tutorials, vol. 22, no. 2, pp. 1191–1221, Jan. 2020, doi: 10.1109/comst.2019.2962586.

[19] M. Usama et al., “Unsupervised machine learning for networking: techniques, applications and research challenges,” IEEE Access, vol. 7, pp. 65579–65615, Jan. 2019, doi: 10.1109/access.2019.2916648.

[20] M. Usama et al., “Unsupervised machine learning for networking: techniques, applications and research challenges,” IEEE Access, vol. 7, pp. 65579–65615, Jan. 2019, doi: 10.1109/access.2019.2916648.

[21] M. Z. Alom et al., “A State-of-the-Art Survey on Deep learning theory and architectures,” Electronics, vol. 8, no. 3, p. 292, Mar. 2019, doi: 10.3390/electronics8030292.

[22] N. Komninos, E. Philippou, and A. Pitsillides, “Survey in Smart Grid and Smart Home Security: Issues, challenges and countermeasures,” IEEE Communications Surveys & Tutorials, vol. 16, no. 4, pp. 1933–1954, Jan. 2014, doi: 10.1109/comst.2014.2320093.

[23] P. Sethi and S. R. Sarangi, “Internet of Things: architectures, protocols, and applications,” Journal of Electrical and Computer Engineering, vol. 2017, pp. 1–25, Jan. 2017, doi: 10.1155/2017/9324035.

[24] P. Sethi and S. R. Sarangi, “Internet of Things: architectures, protocols, and applications,” Journal of Electrical and Computer Engineering, vol. 2017, pp. 1–25, Jan. 2017, doi: 10.1155/2017/9324035.

[25] xxv.S. A. Bakhsh, M. A. Khan, F. Ahmed, M. S. Alshehri, H. Ali, and J. Ahmad, “Enhancing IoT network security through deep learning-powered Intrusion Detection System,” Internet of Things, vol. 24, p. 100936, Dec. 2023, doi: 10.1016/j.iot.2023.100936.

[26] Statista, “Number of IoT connections worldwide 2022-2033, with forecasts to 2030,” Statista, Jun. 12, 2024. https://www.statista.com/statistics/1183457/iot-connected-devices-worldwide/

[27] U. Rührmair, C. Jaeger, M. Bator, M. Stutzmann, P. Lugli, and G. Csaba, “Applications of High-Capacity Crossbar memories in cryptography,” IEEE Transactions on Nanotechnology, vol. 10, no. 3, pp. 489–498, May 2011, doi: 10.1109/tnano.2010.2049367.

[28] W. J. Murdoch, C. Singh, K. Kumbier, R. Abbasi-Asl, and B. Yu, “Definitions, methods, and applications in interpretable machine learning,” Proceedings of the National Academy of Sciences, vol. 116, no. 44, pp. 22071–22080, Oct. 2019, doi: 10.1073/pnas.1900654116.

[29] xxix.Z. Li, W. Fang, C. Zhu, Z. Gao, and W. Zhang, “AI-Enabled trust in distributed networks,” IEEE Access, vol. 11, pp. 88116–88134, Jan. 2023, doi: 10.1109/access.2023.3306452.

@article{1709373,
      author = {James O. Ogunseye, Afolabi Emmanuel},
      title = {Enhancing IoT Network Security Through Deep Learning-Powered Intrusion Detection System},
      journal = {Iconic Research And Engineering Journals},
      year = {2025},
      volume = {8},
      number = {12},
      pages = {1453-1458},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1709373.pdf},
      abstract = {The explosive growth of the Internet of Things (IoT) has introduced increased connectivity and automation with increased cybersecurity threats, especially on resource-constrained devices. Traditional intrusion detection systems (IDS) cannot respond to these dynamic threats due to their reliance on known attack patterns and lack of adaptability. This work investigates the application of deep learning (DL) techniques, in the form of a hybrid Convolutional Neural Network?Long Short-Term Memory (CNN-LSTM) model, to improve IDS performance for IoT networks. The proposed model was evaluated and tested on three publicly available benchmark datasets?CICIoT2023, Bot-IoT, and UNSW-NB15?and has been shown to have improved accuracy, precision, and recall compared to traditional machine learning models. Despite DL's typically high computational requirements, the model showed efficient performance with minimum latency, and it was a suitable candidate for the edge. The results confirm the potential of DL-based IDS to provide robust, adaptive, and elastic solutions for IoT security against new and traditional cyberattacks},
      keywords = {Internet of Things (IoT), Intrusion Detection System (IDS), Deep Learning, CNN-LSTM, Cybersecurity, Network Security, Edge Computing, Anomaly Detection},
      month = {June},
  }