AI-DRIVEN APPROACHES FOR ENHANCING IOT SECURITY: A COMPREHENSIVE EVALUATION

Abstract

As the Internet of Things (IoT) continues to expand ensuring cybersecurity has become paramount due to the rising frequency and sophistication of cyber-attacks. To address this challenge, integrating artificial intelligence (AI) technology with security protocols has emerged as a potential strategy for bolstering IoT cybersecurity defences. This review assesses the effectiveness of combining AI approaches with security mechanisms to mitigate cyber threats in IoT environments. First, the study assesses the effectiveness of AI-driven intrusion detection systems (IDS) in detecting and preventing hostile activity across various IoT network architectures. Second, it examines how AI methods like ML and DL detect unusual behaviour and potential cyber-attacks in real-time within IoT systems. Furthermore, the research looks at how AI-based security measures may adapt and scale to handle dynamic cyber threats and network infrastructures. Additionally, the study investigates the consequences of data privacy and ethical considerations when incorporating AI approaches into IoT security measures, addressing concerns about algorithmic bias and data privacy. Overall, this review sheds light on how AI approaches strengthen security mechanisms, reduce cybersecurity risks, and contribute to the evolving landscape of cyber defence measures in the realm of IoT.