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<p>The rapid proliferation of Internet of Things (IoT) devices has intensified the need for intelligent and adaptive security mechanisms capable of addressing emerging cyber threats such as multimedia data manipulation and zero-day attacks. This paper presents a novel AI-driven intrusion detection framework that synergistically integrates Generative Adversarial Networks (GANs), Deep Q Networks (DQNs), and Federated Learning (FL) to achieve decentralized, privacy-preserving, and real-time threat detection. Unlike existing GAN-based or reinforcement learning (RL)-based models that operate under centralized architectures, the proposed system introduces a hierarchical Edge-Fog-Cloud (EFC) design to minimize latency, enhance scalability, and support distributed intelligence across IoT environments. GANs generate synthetic attack data to augment scarce training samples, while DQNs adaptively learn optimal defensive strategies against dynamic and evolving threats. Federated Learning ensures that sensitive IoT data remain local by transmitting only encrypted model updates, thereby strengthening privacy protection. Experimental results demonstrate that the proposed framework achieves a 93% detection accuracy, reduces latency by approximately 20%, and maintains a privacy preservation rate of 85% compared to state-of-the-art methods. These outcomes highlight the framework’s potential to provide a scalable, adaptive, and privacy-aware solution for real-time intrusion detection in heterogeneous IoT networks.</p>

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A GAN-DQN enhanced AI-driven framework for real-time smart intrusion detection system in cooperative IoT networks

  • 期刊论文

  • 发表日期：

    2026年2月2日

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  • Gunupudi Rajesh Kumar,
  • Ganesh Naidu Ummadisetti,
  • Nimmala Mangathayaru,
  • Yalla Jeevan Nagendra Kumar,
  • Ravikiran K,
  • Krishna Bhimaavarapu

摘要

The rapid proliferation of Internet of Things (IoT) devices has intensified the need for intelligent and adaptive security mechanisms capable of addressing emerging cyber threats such as multimedia data manipulation and zero-day attacks. This paper presents a novel AI-driven intrusion detection framework that synergistically integrates Generative Adversarial Networks (GANs), Deep Q Networks (DQNs), and Federated Learning (FL) to achieve decentralized, privacy-preserving, and real-time threat detection. Unlike existing GAN-based or reinforcement learning (RL)-based models that operate under centralized architectures, the proposed system introduces a hierarchical Edge-Fog-Cloud (EFC) design to minimize latency, enhance scalability, and support distributed intelligence across IoT environments. GANs generate synthetic attack data to augment scarce training samples, while DQNs adaptively learn optimal defensive strategies against dynamic and evolving threats. Federated Learning ensures that sensitive IoT data remain local by transmitting only encrypted model updates, thereby strengthening privacy protection. Experimental results demonstrate that the proposed framework achieves a 93% detection accuracy, reduces latency by approximately 20%, and maintains a privacy preservation rate of 85% compared to state-of-the-art methods. These outcomes highlight the framework’s potential to provide a scalable, adaptive, and privacy-aware solution for real-time intrusion detection in heterogeneous IoT networks.