SBE: Small: An optimization framework for prioritizing cyber-security mitigations for securing information technology infrastructure

我国的信息技术(IT)基础设施容易受到众多安全风险的影响，包括IT供应链中的安全漏洞。这项研究解决了联邦IT基础设施中存在的网络安全风险和漏洞。它将为在预算有限的环境中确定IT安全缓解的优先顺序和部署IT安全缓解提供新的见解。它还将开发可供联邦决策者和其他进行投资的大型组织使用的工具。这些工具使您能够在众多潜在选项中确定降低威胁和保护IT基础设施的经济高效的安全实施的优先顺序。这项研究将引入新的模型，这些模型捕获了确定IT安全缓解措施优先顺序的关键方面。新的优化模型被描述为混合整数线性规划模型、稳健优化模型和双层规划阻断模型。它们捕获敌意攻击路径、重叠的安全缓解能力、多个标准之间的权衡、对数据不确定性的稳健性以及自适应对手的影响。具有自适应对手的模型是一个主要关注点，因此，该模型探索了一系列对抗性战略的复杂性。方法论方面的贡献包括对模型特征的分析，与所提出的近似算法相关的性能保证，提高模型求解能力的新的有效的不等式，以及获得对模型中使用的函数的不确定性稳健的解的新技术。总体目标是通过确定在成本、威胁减少和后果缓解方面有效的安全缓解措施的正确组合来保护联邦IT基础设施。

Our nation's information technology (IT) infrastructure is vulnerable to numerous security risks, including security vulnerabilities within the IT supply chain. This research addresses the cyber-security risks and vulnerabilities that exist in the Federal IT infrastructure. It will provide new insights for prioritizing and deploying IT security mitigations in a budget-constrained environment. It will also develop tools that can be used by Federal decision-makers and other large organizations which make investments. These tools enable prioritizing, among numerous potential options, the cost-effective security implementations which reduce threats and secure IT infrastructure. The research will introduce new models that capture the key facets of prioritizing IT security mitigations. The new optimization models are formulated as mixed integer linear programming models, robust optimization models, and bi-level programming interdiction models. They capture adversarial attack paths, overlapping security mitigation capabilities, tradeoffs between multiple criteria, robustness to data uncertainties, and the impact of adaptive adversaries. Models with adaptive adversaries are a major focus, and therefore, the models explore a range of adversarial strategic sophistication. The methodological contributions include an analysis of the model features, performance guarantees associated with proposed approximation algorithms, new valid inequalities to improve the ability to solve the models, and new techniques to obtain solutions robust to uncertainty of the functions used in a model. The overall goal is to protect Federal IT infrastructure by identifying the right mix of security mitigations that is effective with respect to cost, threat reduction, and consequence mitigation.