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

SBE：小型：优先考虑网络安全缓解措施以确保信息技术基础设施安全的优化框架

• 批准号: 1422768
• 负责人: Laura Albert
• 金额: $ 44.78万
• 依托单位: University of Wisconsin-Madison
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-01 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1422768&HistoricalAwards=false
• 关键词: SBE; Small; optimization; framework; prioritizing

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.

我国的信息技术（IT）基础设施易受众多安全风险的影响，包括IT供应链中的安全漏洞。本研究旨在解决联邦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优先级和部署It安全缓解措施提供新的见解。它还将开发可供联邦决策者和其他大型投资组织使用的工具。这些工具支持在众多潜在选项中对经济有效的安全实现进行优先排序，从而减少威胁并保护IT基础设施。该研究将引入新的模型，捕捉优先考虑IT安全缓解的关键方面。新的优化模型包括混合整数线性规划模型、鲁棒优化模型和双层规划阻断模型。它们捕获对抗性攻击路径、重叠的安全缓解功能、多个标准之间的权衡、对数据不确定性的鲁棒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的焦点，因此，这些模型探索了一系列对抗性战略的复杂性。方法上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，提高求解模型能力的新的有效不等式，以及获得对模型中使用的函数不确定性的鲁棒解的新技术。总体目标是通过确定在成本、减少威胁和减轻后果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。