Securing IoT Communications with Genai-Based Threat Simulation and Defense

Abstract

The rapid development of the Internet of Things (IoT) has also brought significant security and privacy concerns due to the heterogeneous nature, limited resources of the devices, and the absence of standardized security practices. The existing intrusion detection systems and the rule-based security systems remain reactive and IoT networks can be targeted by adaptive and zero-day attacks. This paper proposes a Generative Artificial Intelligence (GenAI)-based system that can be applied to model the dynamic cyber threat and automatically improve IoT communications security. The framework integrates conditional generative adversarial networks (cGANs) and large language models (LLMs) to create realistic attack scenarios and continuously refresh defense strategies in real time. The experimental testing demonstrates the improved accuracy of detection (97.2 percent), reduced false negatives (4.2 percent), and reduced response time (68 ms) compared to the conventional baselines, which indicates the flexibility and stability of the system. The key advantages of this approach are proactive threat anticipation, self-adaptive self-organization, and zero-day attack resilience. However, the framework is computationally demanding when deployed on low-end IoT devices and should be further tested in large-scale real-life environments. The possible real-world applications of the work are smart healthcare, transportation and industrial IoT settings where proactive security is required. The work also contributes to the creation of AI-based cybersecurity as it suggests a scalable and adaptive model of defense which surmounts the limitations of the static security paradigm.