An Adaptive AI-Driven Cyber Threat Detection Framework for Securing Heterogeneous IoT Networks

Authors

  • Kireet Muppavaram Department of CSE, GITAM School of Technology, GITAM Deemed to be University, Hyderabad Campus, Telangana, India
  • T. Aruna Sri Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Hyderabad Campus, Telangana, India
  • T. Murali Krishna Department of CSE, Ashoka Womens Engineering College, Kurnool, Andhra Pradesh, India
  • Jyotsnarani Tripathi Department of CSE (AIML & IoT), VNR Vignana Jyothi Institute of Engineering and Technology, India
  • Manmath Nath Das Department of AI & DS, VNR Vignana Jyothi Institute of Engineering and Technology, Hyderabad, India
  • Sharada Mani Department of CSE-DS, QIS College of Engineering and Technology (A), AP, India
  • G. Lakshmi Vara Prasad Department of AIML, QIS College of Engineering and Technology (A), AP, India
  • T. Manyam School of Engineering, Information Technology, Anurag University, Telangana, India
Volume: 15 | Issue: 5 | Pages: 26750-26756 | October 2025 | https://doi.org/10.48084/etasr.12386

Received: 26 May 2025 | Revised: 18 June 2025 and 8 July 2025 | Accepted: 11 July 2025 | Online: 5 August 2025


Corresponding author: Kireet Muppavaram

Abstract

This work proposes an intelligent cybersecurity system built upon Artificial Intelligence (AI) to address evolving cyber threats in heterogeneous Internet of Things (IoT) environments. The proposed framework integrates machine learning with mathematical threat analysis to shift from traditional system security, which responds after an attack, to a proactive approach that predicts and prevents threats. It reacts immediately, processes in just 0.35 s, adapts to 95% of IoT surroundings, and handles security by categorizing threats into four tiers with minimal impact on performance. Tests against standard Intrusion Detection Systems (IDSs), such as SNORT, Suricata, and Bro/Zeek, demonstrate that the framework is superior at handling a wide range of threats.

Keywords:

cyber threats, cybersecurity, Artificial Intelligence (AI), Internet of Things (IoT), machine learning

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How to Cite

[1]
K. Muppavaram, “An Adaptive AI-Driven Cyber Threat Detection Framework for Securing Heterogeneous IoT Networks”, Eng. Technol. Appl. Sci. Res., vol. 15, no. 5, pp. 26750–26756, Oct. 2025.

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Copyright (c) 2025 Kireet Muppavaram, T. Aruna Sri, T. Murali Krishna, Sharada Mani, Jyotsnarani Tripathi, Manmath Nath Das, G. Lakshmi Vara Prasad, T. Manyam

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