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2024 IEEE 3rd International Conference on AI in Cybersecurity (ICAIC)最新文献

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Prescriptive Analytics-based Robust Decision-Making Model for Cyber Disaster Risk Reduction 基于规范性分析的降低网络灾难风险稳健决策模型
Pub Date : 2024-02-07 DOI: 10.1109/ICAIC60265.2024.10433837
Joseph Ponnoly, John Puthenveetil, Patricia D’Urso
Decision-making in cyber security attack scenarios involves deep uncertainty and adversarial decision-making. Robust Decision Making (RDM) uses a structured approach to evaluate the performance of various decision strategies under conditions of deep uncertainty to enable adaptive decision-making. The challenge is in getting reliable inputs to the RDM model. It is suggested that prescriptive analytics enabled by predictive analytics including big data analytics, reinforcement learning, and Monte Carlo simulations, could provide decision-makers with various options to make informed and robust decision-making. Prescriptive analytics based RDM model is proposed for cyber disaster risk reduction. The model extends the proactive cyber defense model based on sensing and sensemaking of early warning signs of cyber disasters.
网络安全攻击场景中的决策涉及深度不确定性和对抗性决策。稳健决策(RDM)采用一种结构化方法来评估深度不确定性条件下各种决策策略的性能,以实现适应性决策。挑战在于如何为 RDM 模型提供可靠的输入。有人建议,由预测分析(包括大数据分析、强化学习和蒙特卡洛模拟)支持的规范性分析可为决策者提供各种选择,以做出明智、稳健的决策。为降低网络灾难风险,提出了基于预测分析的 RDM 模型。该模型基于对网络灾难早期预警信号的感知和感知决策,扩展了主动网络防御模型。
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2024 IEEE 3rd International Conference on AI in Cybersecurity (ICAIC)
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