威胁情报中部分信息下网络漏洞优先级排序的鲁棒统计框架
A robust statistical framework for cyber-vulnerability prioritisation under partial information in threat intelligence
AI总结：
针对网络漏洞评估中的不确定性问题，提出基于中分位数回归的鲁棒统计框架，设计部分信息下的排序不变准确率指标，经模拟与真实数据验证，可为威胁情报决策提供支撑。
摘要：
主动网络风险评估正日益受到重视，因为众多行业都能通过防范网络事件、保障数据的完整性、机密性和可用性而获益。人们对网络安全的关注度不断上升，也源于信息物理系统的互联互通程度日益提高，这给新出现的网络漏洞带来了多方面的不确定性。本研究提出了一个鲁棒统计框架，用于在网络漏洞及其优先级排序存在不确定性的情况下开展定量与定性推理。具体而言，我们利用中分位数回归处理有序风险评估，并将其与当前用于网络风险排序和分级响应的替代方法进行对比。为此，我们提出了一种新的准确率度量指标，适用于在仅部分掌握全部现有漏洞集合的情况下保持排序不变性。该模型在模拟数据以及来自精选数据库的真实数据上进行了测试，这些数据库可支撑真实场景下网络漏洞的评估、利用或响应工作。借助这些数据集，我们对比了多种模型和准确率度量指标，讨论了在运营场景中，对网络漏洞的部分认知对威胁情报和决策制定的影响。
英文摘要：
Proactive cyber-risk assessment is gaining momentum due to the wide range of sectors that can benefit from the prevention of cyber-incidents by preserving integrity, confidentiality, and the availability of data. The rising attention to cybersecurity also results from the increasing connectivity of cyber-physical systems, which generates multiple sources of uncertainty about emerging cyber-vulnerabilities. This work introduces a robust statistical framework for quantitative and qualitative reasoning under uncertainty about cyber-vulnerabilities and their prioritisation. Specifically, we take advantage of mid-quantile regression to deal with ordinal risk assessments, and we compare it to current alternatives for cyber-risk ranking and graded responses. For this purpose, we identify a novel accuracy measure suited for rank invariance under partial knowledge of the whole set of existing vulnerabilities. The model is tested on both simulated and real data from selected databases that support the evaluation, exploitation, or response to cyber-vulnerabilities in realistic contexts. Such datasets allow us to compare multiple models and accuracy measures, discussing the implications of partial knowledge about cyber-vulnerabilities on threat intelligence and decision-making in operational scenarios.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
