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<p>The advent of 6G networks is that , they promise to deliver high connectivity as well as a much larger attack surface, traditional security models are found to be wanting. In this paper, we propose an Adaptive Decoy Generation Framework that is able to deliver proactive, intelligent, as well as adaptive security solutions at the network edge. The proposed framework comprises three main intelligent components that include a Conditional Generative Adversarial Network (cGAN), a Reinforcement Learning (RL) agent that is based on Proximal Policy Optimization (PPO), as well as an adversarial feedback mechanism that allows the system to learn as well as adapt to new attacks. The simulation results using the CIC-IoT-2023 dataset showed that the proposed framework is able to deliver robust security solutions since it is able to achieve a 97.9% detection rate with a 1.3% false positive rate as well as a 15 ms detection latency on average. The system has a positive Adaptability Index (+5.2%), thereby demonstrating that it is able to defend against intelligent as well as learning-based attacks, thereby being more secure than traditional security models. The proposed framework is able to create a new paradigm in delivering intelligent as well as adaptive security solutions that will be able to cater to 6G networks.</p>

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Self-defending 6G networks through AI-driven adaptive decoy generation at the edge

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月26日

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  • Ranil Mukesh M J,
  • Pandiya Rajan G,
  • Prabhu Gopal,
  • Malathy Sathyamoorthy,
  • Aniket S. Nagane,
  • Rakesh Keshava

摘要

The advent of 6G networks is that , they promise to deliver high connectivity as well as a much larger attack surface, traditional security models are found to be wanting. In this paper, we propose an Adaptive Decoy Generation Framework that is able to deliver proactive, intelligent, as well as adaptive security solutions at the network edge. The proposed framework comprises three main intelligent components that include a Conditional Generative Adversarial Network (cGAN), a Reinforcement Learning (RL) agent that is based on Proximal Policy Optimization (PPO), as well as an adversarial feedback mechanism that allows the system to learn as well as adapt to new attacks. The simulation results using the CIC-IoT-2023 dataset showed that the proposed framework is able to deliver robust security solutions since it is able to achieve a 97.9% detection rate with a 1.3% false positive rate as well as a 15 ms detection latency on average. The system has a positive Adaptability Index (+5.2%), thereby demonstrating that it is able to defend against intelligent as well as learning-based attacks, thereby being more secure than traditional security models. The proposed framework is able to create a new paradigm in delivering intelligent as well as adaptive security solutions that will be able to cater to 6G networks.