I-Corps: Securing the Machine Learning Lifecycle and Providing Artificial Intelligence Assurance

I-Corps：保护机器学习生命周期并提供人工智能保障

• 批准号: 2107443
• 负责人: Benjamin Harvey
• 金额: $ 5万
• 依托单位: George Washington University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-03-01 至 2022-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2107443&HistoricalAwards=false
• 关键词: Corps; Securing; Machine; Learning; Lifecycle

The broader impact of this I-Corps project is the development of a framework to securely develop Artificial Intelligence/Machine Learning (AI/ML). As the vulnerability of the AI/ML lifecycle becomes increasingly apparent, organizations that rely on the consistency and integrity of their AI/ML are faced with the difficult task of assuring AI/ML security. Along with challenges involved with tailoring the concept of zero trust into such environments, assuring security also needs to be accomplished quickly. The cost to develop and implement a single AI/ML model can reach hundreds of thousands of dollars, and the malicious alteration of AI/ML models, model features, and training data that effectively poison AI/ML can negate the return on this substantial investment. Such poisoning attacks are a nascent threat that is expected to become widespread as malicious actors gain technical ability. This susceptibility of AI/ML to poisoning attacks poses a severe national security concern as AI/ML has become central to mission-critical defense and intelligence capacities. This I-Corps project is based on the development of a lifecycle management tool utilizing blockchain technology. The proposed innovation will enable users to store, train, and deploy data, AI/ML models, and model features, with each transaction or attempted transaction. These transactions will be immutably logged to provide an audit trail, ensuring that nothing stored on the blockchain can be surreptitiously altered. These project goals are to determine how the use of blockchain technology may enhance the security of the AI/ML lifecycle. The technology may be used to analyze the AI/ML lifecycle security concerns and foci of stakeholders including intelligence agencies, academia, commercial businesses, and machine learning engineers. The project will also help determine if practical solutions exist for engineers to secure the machine learning lifecycle, examine economic models that inform the tradeoff of security, cost efficiency, awareness of virtual structure as it relates to the concept of “Zero AI Trust”, and test usability in machine learning lifecycle management tools.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

这个i-Corps项目的更广泛影响是开发了一个框架，以安全地开发人工智能/机器学习(AI/ML)。随着AI/ML生命周期的脆弱性日益明显，依赖其AI/ML的一致性和完整性的组织面临着确保AI/ML安全的艰巨任务。除了在这样的环境中量身定做零信任概念所涉及的挑战之外，确保安全也需要迅速完成。开发和实施一个AI/ML模型的成本可能高达数十万美元，恶意篡改AI/ML模型、模型功能和训练数据，从而有效地毒害AI/ML，可能会抵消这一巨额投资的回报。这种中毒攻击是一种新生的威胁，随着恶意攻击者获得技术能力，预计这种威胁将会广泛传播。AI/ML对中毒攻击的这种敏感性构成了严重的国家安全担忧，因为AI/ML已经成为关键任务防御和情报能力的核心。这个i-Corps项目是基于利用区块链技术开发的生命周期管理工具。拟议的创新将使用户能够在每笔交易或尝试的交易中存储、培训和部署数据、AI/ML模型和模型功能。这些交易将被不变地记录下来，以提供审计跟踪，确保区块链上存储的任何东西都不会被秘密更改。这些项目目标是确定区块链技术的使用如何增强AI/ML生命周期的安全性。该技术可用于分析AI/ML生命周期安全问题和利益相关者的关注点，包括情报机构、学术界、商业企业和机器学习工程师。该项目还将帮助确定是否存在针对工程师的实用解决方案，以确保机器学习生命周期的安全，检查提供安全、成本效率、虚拟结构意识与“零人工智能信任”概念相关的经济模型，并测试机器学习生命周期管理工具的可用性。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。