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<p>Cyber range environments are key platforms for cybersecurity training, research and testing. This can enable the emulation of realistic cyberattacks and incident response scenarios. Most of the traditional approaches to simulation are based on predefined or rule-based models. These approaches do not allow for adaptation and fail to account for the complexity of evolving threats. An artificial Intelligence-Driven Multi-Agent System (MAS) has been proposed in this paper. The framework autonomously simulates sophisticated cyberattacks and coordinates automated incident response within a cyber range. CICIDS2017 and UNSW-NB15 datasets are combined and integrated into a cyber range simulator CyDER 2.0. Reinforcement learning and anomaly detection methods are used to enable attack and defence agents for adaptive behaviours. The MAS architecture implements realistic attack vectors and response strategies. A set of experiments demonstrate that the AI-driven MAS achieves much higher simulation realism and responsiveness than the traditional static systems. This method also has higher detection accuracy with minimal mitigation times. The model undergoes rigorous validation and acceptance testing to assess robustness and generalizability.</p>

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Artificial intelligence driven multi agent framework for adaptive cyber attack simulation and automated incident response in cyber range environments

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月7日

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  • Alka Agrawal,
  • Mohd Nadeem,
  • Ahmed Al Nuaim,
  • Abdullah Al Nuaim

摘要

Cyber range environments are key platforms for cybersecurity training, research and testing. This can enable the emulation of realistic cyberattacks and incident response scenarios. Most of the traditional approaches to simulation are based on predefined or rule-based models. These approaches do not allow for adaptation and fail to account for the complexity of evolving threats. An artificial Intelligence-Driven Multi-Agent System (MAS) has been proposed in this paper. The framework autonomously simulates sophisticated cyberattacks and coordinates automated incident response within a cyber range. CICIDS2017 and UNSW-NB15 datasets are combined and integrated into a cyber range simulator CyDER 2.0. Reinforcement learning and anomaly detection methods are used to enable attack and defence agents for adaptive behaviours. The MAS architecture implements realistic attack vectors and response strategies. A set of experiments demonstrate that the AI-driven MAS achieves much higher simulation realism and responsiveness than the traditional static systems. This method also has higher detection accuracy with minimal mitigation times. The model undergoes rigorous validation and acceptance testing to assess robustness and generalizability.