Multi-Layered AI-Driven Security in Wireless Ecosystems

Year : 2026 | Volume : 04 | Issue : 01 | Page : 21 28
    By

    Heena T. Shaikh,

  • Kazi Kutubuddin Sayyad Liyakat,

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

Abstract

The proliferation of next-generation wireless technologies, from 5G/6G networks to the pervasive Internet of Things (IoT), has birthed a hyperconnected digital ecosystem of unprecedented scale and dynamism. This interconnectedness, however, introduces a vast and volatile attack surface, rendering conventional, signature-based security paradigms fundamentally obsolete. This paper posits that the only viable defense is an offensive, self-adaptive one, predicated on the integration of artificial intelligence (AI) directly into the wireless security fabric. A multi-layered AI framework is proposed, integrating complementary machine learning (ML), deep learning (DL), and adaptive reasoning models across four orthogonal security strata: real-time threat detection, predictive risk modeling, autonomous response orchestration, and federated learning for privacy-preserving collaboration. This framework operationalizes defense-in-depth principles while enabling proactive, self-healing resilience. Our simulation, conducted across a heterogeneous 5G-IoT testbed, demonstrates a 98.7% detection rate for anomalous traffic patterns, a 92% reduction in false positives compared to heuristic models, and a proactive prediction of network vulnerability exploits with an average lead time of 4.3 hours. The findings conclude that an AI-native approach is not a mere enhancement but a fundamental re-architecting of wireless ecosystem security, transitioning from a reactive posture to a predictive, self-healing, and resilient digital immune system.

Keywords: Artificial intelligence, detection rate, false positive, multi-layered, wireless

[This article belongs to International Journal of Wireless Security and Networks ]

How to cite this article:
Heena T. Shaikh, Kazi Kutubuddin Sayyad Liyakat. Multi-Layered AI-Driven Security in Wireless Ecosystems. International Journal of Wireless Security and Networks. 2026; 04(01):21-28.
How to cite this URL:
Heena T. Shaikh, Kazi Kutubuddin Sayyad Liyakat. Multi-Layered AI-Driven Security in Wireless Ecosystems. International Journal of Wireless Security and Networks. 2026; 04(01):21-28. Available from: https://journals.stmjournals.com/ijwsn/article=2026/view=237520


References

  1. Liyakat KK, Halli UM. Nanotechnology in IoT security. J Nanoscience 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):183–184.
  4. Liyakat KK. Detection of malicious nodes in IoT networks based on packet loss using ML. J Mobile Comput Commun Mobile Netw. 2022;9:9–17.
  5. Nikita K, Supriya J. Design of vehicle system using CAN protocol. Int J Res Appl Sci Eng Technol. 2020;8:1978–1983.
  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. Pol RS, Deshmukh AB, Jadhav MM, Liyakat KK, Mulani AO. Ibutton based physical access authorization and security system. J Algebraic Stat. 2022; 13(3): 3822–3829.
  9. 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.
  10. Chinthamu N, Prasad M, Chinchawade AJ, Liyakat KKS, Deepti K, Karukuri M, Kumar CM. Self-secure firmware model for blockchain-enabled IoT environment to embedded system. Eur Chem Bull. 2023;12(Suppl 3):653–660. doi:10.31838/ecb/2023.12.s3.075.
  11. Saputra A, Wang G, Zhang JZ, Behl A. The framework of talent analytics using big data. TQM J. 2022;34(1):178–198. doi:10.1108/TQM-03-2021-0089.
  12. Akkaoui R, Stefanov A, Palensky P, Epema DHJ. Resilient, auditable, and secure IoT-enabled smart inverter firmware amendments with blockchain. IEEE Internet Things J. 2024;11(5):8945–8960. doi:10.1109/JIOT.2023.3321954.
  13. Gund VD. PIR sensor-based Arduino home security system. J Instrum Innov Sci. 2023;8:33–37.
  14. Al-Mashhadi HM, Alabiech MH. A survey of email service: Attacks, security methods and protocols. Int J Comput Appl. 2017;162(11):31–40. doi:10.5120/ijca2017913417.
  15. Altmann J. Military uses of nanotechnology: Perspectives and concerns. Secur Dialogue. 2004;35:61–79. doi:10.1177/0967010604042536.
Regular Issue Subscription Review Article
Volume 04
Issue 01
Received 16/01/2026
Accepted 17/01/2026
Published 24/02/2026
Publication Time 39 Days
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