Generative AI for Internet of Things Security: Challenges and Opportunities

2025-02-13

AI Summary

This paper addresses the security bottlenecks in Internet of Things (IoT) systems arising from device heterogeneity, resource constraints, and dynamically evolving threats. To bridge this gap, it systematically investigates the feasibility, efficacy, and practical deployment pathways of generative artificial intelligence (GenAI) for IoT security. The study proposes the first comprehensive GenAI-driven IoT security framework, integrating threat modeling, lightweight anomaly detection, and MITRE ATT&CK/Mitigations mapping. Its validity is empirically validated through three cross-layer case studies—network intrusion detection, firmware vulnerability generation, and automated response strategy synthesis. The work identifies six critical research gaps in GenAI-enabled IoT security and delivers a technically rigorous, engineering-practical roadmap for future advancement. By unifying theoretical insight with deployable mechanisms, this research establishes a methodological foundation and concrete implementation paradigm for the deep integration of GenAI and IoT security.

Technology Category

• Application Domains: Internet of Things, Sensor Networks & Smart Cities
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Computer Vision: Generative Adversarial Networks (GANs) for Vision

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Security and Privacy: Large-scale security measurements
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

As Generative AI (GenAI) continues to gain prominence and utility across various sectors, their integration into the realm of Internet of Things (IoT) security evolves rapidly. This work delves into an examination of the state-of-the-art literature and practical applications on how GenAI could improve and be applied in the security landscape of IoT. Our investigation aims to map the current state of GenAI implementation within IoT security, exploring their potential to fortify security measures further. Through the compilation, synthesis, and analysis of the latest advancements in GenAI technologies applied to IoT, this paper not only introduces fresh insights into the field, but also lays the groundwork for future research directions. It explains the prevailing challenges within IoT security, discusses the effectiveness of GenAI in addressing these issues, and identifies significant research gaps through MITRE Mitigations. Accompanied with three case studies, we provide a comprehensive overview of the progress and future prospects of GenAI applications in IoT security. This study serves as a foundational resource to improve IoT security through the innovative application of GenAI, thus contributing to the broader discourse on IoT security and technology integration.

Problem

• Integrating Generative AI in IoT security
• Exploring GenAI's potential to enhance IoT security measures
• Identifying research gaps in GenAI applications for IoT security

Innovation

• Generative AI enhances IoT security.
• GenAI addresses IoT security challenges.
• Case studies validate GenAI applications.