TARU PUBLICATIONS
Journal of Information and Optimization Sciences cover
Open Access ·Peer-reviewed·ISSN (Online): 2169-0103·ISSN (Print): 0252-2667

WoS  JIF 2026 : 0.4 (Q4)

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Monthly Journal: Publishes theoretical and applied research on topics in information and optimization sciences.

Issues up to 2022 co-published with and available at:Taylor & Francis
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Open Access Research Article

AI-driven cybersecurity for IoT networks : Enhancing threat detection and mitigation

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pp. 1973–1981Vol. 46Issue 6September 2025DOI: 10.47974/JIOS-2026XML
Received:
10 Dec 2024
Published Online:
30 Sep 2025
Article type:
Research Article
Language:
EN
Article no.:
JIOS-2026
Pages:
1973–1981

Abstract

The proliferation of Internet of Things (IoT) devices has increased the size and complexity of cyber security threats to such an extent that traditional security is insufficient to safeguard IoT networks. This research uses Artificial Intelligence (AI) to provide enhanced real-time security through advanced anomaly detection features. The study considers various new methodologies, including federated learning for privacy-preserving but threat detection, graph neural networks (GNNs) to monitor attack propagation, and unsupervised anomaly detection through autoencoders and generative adversarial networks (GANs). In addition, research in zero-trust AI security architecture will also be performed to further secure IoT networks by verifying each access request. Integrating AI-driven approaches delivers an end-to-end, proactive solution for protecting IoT ecosystems against future cyber-attacks.

Keywords

Subject Classifications

68M1268T0168T07

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