Challenges in Implementing AI-Driven Cyber Défense Mechanisms for IoT Applications

Abstract

The rapid proliferation of Internet of Things (IoT) devices has transformed industries and everyday life, but it has also introduced significant cybersecurity risks. Traditional security methods often struggle to address the dynamic and heterogeneous nature of IoT networks. Leveraging Artificial Intelligence (AI) for cyber defense offers promising solutions, including real-time anomaly detection, predictive threat analysis, and automated response mechanisms. However, implementing AI-driven security in IoT environments presents several challenges. These include the limited computational resources of IoT devices, the vast volume and diversity of generated data, privacy concerns, model interpretability, and the evolving sophistication of cyberattacks. This study explores these challenges, highlighting the critical factors that must be addressed to design effective, scalable, and resilient AI-based cyber defense mechanisms for IoT applications. Understanding these issues is essential for advancing secure IoT deployments and ensuring the integrity, availability, and confidentiality of interconnected systems.

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