TY  - EJOU
AU  - Ansari, Arshiya Sajid 
AU  - Altuwaijri, Ghadir 
AU  - Alodhyani, Fahad 
AU  - Ghembaza, Moulay Ibrahim El-Khalil 
AU  - Paramb, Shahabas Manakunnath Devasam 
AU  - Mohammadi, Mohammad Sajid 

TI  - A Detailed Review of Current AI Solutions for Enhancing Security in Internet of Things Applications
T2  - Computers, Materials \& Continua

PY  - 2025
VL  - 83
IS  - 3
SN  - 1546-2226

AB  - IoT has emerged as a game-changing technology that connects numerous gadgets to networks for communication, processing, and real-time monitoring across diverse applications. Due to their heterogeneous nature and constrained resources, as well as the growing trend of using smart gadgets, there are privacy and security issues that are not adequately managed by conventional security measures. This review offers a thorough analysis of contemporary AI solutions designed to enhance security within IoT ecosystems. The intersection of AI technologies, including ML, and blockchain, with IoT privacy and security is systematically examined, focusing on their efficacy in addressing core security issues. The methodology involves a detailed exploration of existing literature and research on AI-driven privacy-preserving security mechanisms in IoT. The reviewed solutions are categorized based on their ability to tackle specific security challenges. The review highlights key advancements, evaluates their practical applications, and identifies prevailing research gaps and challenges. The findings indicate that AI solutions, particularly those leveraging ML and blockchain, offer promising enhancements to IoT privacy and security by improving threat detection capabilities and ensuring data integrity. This paper highlights how AI technologies might strengthen IoT privacy and security and offer suggestions for upcoming studies intended to address enduring problems and improve the robustness of IoT networks.
KW  - Security in IoT applications; privacy-preserving; blockchain; AI-driven security mechanisms

DO  - 10.32604/cmc.2025.064027