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Improved Heatmap Visualization of Pareto-Optimal Set in Multi-Objective Optimization of Defensive Strategy

机译:防御策略多目标优化中帕累托最优集的改进热图可视化

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It is an important task in network security management to use defensive strategy to mitigate security risk. We obtain the Pareto-optimal set of defensive strategy using multi-objective optimization algorithm (MOO) based on attack tree. However, decision maker (DM) may find it problematic to select an appropriate defensive strategy from the Pareto-optimal set, and visualization of the Pareto-optimal set is an effective method. In well-known spectral seriation of heatmap, there exist problem of mapping identical objective values of the Pareto-optimal set to different colors. And there still exist problem of weak flexibility and interactivity while the original spectral seriation of heatmap doesn't provide interface for DM. We improve the clarity by mapping identical objective values to the same color through assigning identical ranked values for identical objective values, and increase interactivity by allowing DM reorder objects and solutions or set interested solutions. Finally, the most suitable defensive strategy will be obtained from the improved heatmap easily. Application and advantages of the proposed approach are illustrated through a case study.
机译:使用防御策略来降低安全风险是网络安全管理中的一项重要任务。我们使用基于攻击树的多目标优化算法(MOO)获得了帕累托最优的防御策略集。但是,决策者(DM)可能会发现从帕累托最优集合中选择适当的防御策略很成问题,而帕累托最优集合的可视化是一种有效的方法。在众所周知的热图谱系列中,存在将帕累托最优集合的相同目标值映射到不同颜色的问题。而且,热图的原始光谱系列不提供DM的接口时,仍然存在柔韧性和交互性较弱的问题。我们通过将相同的目标值分配给相同的目标值,将相同的目标值映射到相同的颜色来提高清晰度,并通过允许DM重新排序对象和解决方案或设置感兴趣的解决方案来提高交互性。最后,可以从改进的热图轻松获得最合适的防御策略。通过案例研究说明了该方法的应用和优点。

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