Multi-Layered AI-Driven Paradigm Shift in IoT Ecosystem Security

Year : 2026 | Volume : 16 | Issue : 01 | Page : 13 21
    By

    Kazi Kutubuddin Sayyad Liyakat,

  • Heena T Shaikh,

  1. Professor, Department of Electronics and Telecommunication Engineering, Brahmdevdada Mane Institute of Technology, Solapur, Maharashtra, India
  2. Assistant. Professor, Department of Electronics and Telecommunication Engineering, Brahmdevdada Mane Institute of Technology, Solapur, Maharashtra, India

Abstract

As the Internet of Things (IoT) continues to weave itself into the fabric of modern life – from smart homes and industrial automation to healthcare and urban infrastructure – the associated security vulnerabilities have become increasingly apparent. Traditional security mechanisms, often built on static rules and perimeter-based defenses, struggle to keep pace with the scale, heterogeneity, and dynamic nature of IoT ecosystems. In response, artificial intelligence (AI) has emerged as a transformative force in fortifying IoT environments against evolving cyber threats. The exponential expansion of the IoT has created an unprecedentedly complex and porous attack surface. Traditional, perimeter-based security paradigms are fundamentally inadequate for this decentralized, heterogeneous ecosystem. This paper proposes a novel framework: the cognitive-autonomic security mesh (CASM). CASM is a multi-layered, AI-native security architecture designed to provide proactive, adaptive, and resilient defense across the entire IoT stack – from the constrained endpoint to the cloud and back. It leverages a synergistic interplay of distributed AI models to achieve a state of continuous self-learning, self-healing, and autonomous threat mitigation. By continuously learning from device interactions, network traffic patterns, and environmental inputs, the proposed method transcends rule-based limitations, enabling proactive defense mechanisms that evolve alongside emerging attack vectors. Deployed across edge, fog, and cloud layers, this AI-integrated approach enhances scalability, reduces false positives, and strengthens end-to-end security across distributed IoT networks.

Keywords: Accuracy, artificial intelligence, CASH, F1 score, IoT, multi-layered ai, precision, recall, security, throughput

[This article belongs to Journal of Communication Engineering & Systems ]

How to cite this article:
Kazi Kutubuddin Sayyad Liyakat, Heena T Shaikh. Multi-Layered AI-Driven Paradigm Shift in IoT Ecosystem Security. Journal of Communication Engineering & Systems. 2026; 16(01):13-21.
How to cite this URL:
Kazi Kutubuddin Sayyad Liyakat, Heena T Shaikh. Multi-Layered AI-Driven Paradigm Shift in IoT Ecosystem Security. Journal of Communication Engineering & Systems. 2026; 16(01):13-21. Available from: https://journals.stmjournals.com/joces/article=2026/view=242006


References

  1. Liyakat KK, Halli UM. Nanotechnology in IoT security. J Nanosci Nanoeng Appl. 2022;12(3):11–16.
  2. Devanand WA, Raghunath RD, Baliram AS, Kazi K. Smart agriculture system using IoT. Int J Innov Res Technol. 2019;5(10):480–483.
  3. Hotkar PR, Kulkarni V, Kamble P, Kazi KS. Implementation of low power and area efficient carry select adder. Int J Res Eng Sci Manag. 2019;2(4):183–84.
  4. Khatun MA, Chowdhury N, Uddin MN. Malicious nodes detection based on artificial neural network in IoT environments. 2019 22nd International Conference on Computer and Information Technology (ICCIT), Dhaka, Bangladesh. 2019. p. 1–6. DOI: 10.1109/ICCIT48885.2019.9038563.
  5. Nikita K, Supriya J. Design of vehicle system using CAN protocol. Int J Res Appl Sci Eng Technol. 2020;8:1978–83.
  6. Kundaliya BL, Hadia SK. Routing algorithms for wireless sensor networks: analysed and compared. Wirel Pers Commun. 2020;110(1):85–107. DOI: 10.1007/s11277-019-06713-3.
  7. Akansha K. Email security. J Image Process Intell Remote Sens. 2022;2(6):295–320.
  8. Sanchez-Reillo R, Gonzalez-Marcos A. Access control system with hand geometry verification and smart cards. IEEE Aerosp Electron Syst Mag. 2000;15(2):45–48. DOI: 10.1109/62.825671.
  9. Liu L, Yang J, Meng W. Detecting malicious nodes via gradient descent and support vector machine in Internet of Things. Comput Electr Eng. 2019;77:339–53. DOI: 10.1016/j.compeleceng.2019.06.013.
  10. Son M, Kim H. Blockchain-based secure firmware management system in IoT environment. 2019 21st International Conference on Advanced Communication Technology (ICACT), PyeongChang, Korea (South). 2019. p. 142–46. DOI: 10.23919/ICACT.2019.8701959.
  11. Jain N, Maitri. Big data and predictive analytics: a facilitator for talent management. In: Munshi UM, Verma N, editors. Data Science Landscape: Towards Research Standards and Protocols. Singapore: Springer; 2018. p. 199–204. DOI: 10.1007/978-981-10-7515-5_14.
  12. Lee B, Lee JH. Blockchain-based secure firmware update for embedded devices in an Internet of Things environment. J Supercomput. 2017;73(3):1152–67. DOI: 10.1007/s11227-016-1870-0.
  13. Golder A, Gupta D, Roy S, Al Ahasan MA, Haque MA. GSM based home security alarm system using Arduino using mobile call. 2023 IEEE 14th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON), New York, NY, USA. 2023. p. 268–74. DOI: 10.1109/UEMCON59035.2023.10316089.
  14. Al-Mashhadi HM, Alabiech MH. A survey of email service: attacks, security methods and protocols. Int J Comput Appl. 2017 Mar;162(11):31–40. DOI: 10.5120/ijca2017913417.
  15. Altmann J. Military uses of nanotechnology: perspectives and concerns. Secur Dialogue. 2004;35(1):61–79. DOI: 10.1177/0967010604042536.
Regular Issue Subscription Review Article
Volume 16
Issue 01
Received 16/01/2026
Accepted 19/01/2026
Published 14/04/2026
Publication Time 88 Days
118

Login


My IP

PlumX Metrics