NSF Convergence Accelerator Track - Track D - AI-Enabled, Privacy-Preserving Information Sharing for Securing Network Infrastructure

NSF 融合加速器轨道 - 轨道 D - 支持人工智能、保护隐私的信息共享，以确保网络基础设施的安全

• 批准号: 2040675
• 负责人: Giulia Fanti
• 金额: $ 96.8万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-15 至 2022-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2040675&HistoricalAwards=false
• 关键词: NSF; Convergence; Accelerator; Track; AI

The NSF Convergence Accelerator supports use-inspired, team-based, multidisciplinary efforts that address challenges of national importance and will produce deliverables of value to society in the near future. Cyber attacks on enterprise networks pose a tremendous threat to business operations today. Defending against the ever-changing landscape of threats and normal user traffic is time-consuming and labor-intensive. To address this challenge, there is an ongoing effort across many sectors to adopt artificial intelligence and machine learning (AI/ML) models to automate security incident detection and response. In practice, however, there are two roadblocks to AI/ML-enabled workflows: (1) lack of sufficient data to train a reliable model to detect new attack campaigns or model normal behaviors; (2) lack of confidence in model outputs over a short timeframe, inducing undesirable tradeoffs between false positives (i.e., blocking legitimate users) and false negatives (i.e., missing attacks). Ideally, sharing data would help address both of these problems, however this information is rarely shared (if at all) due to concerns about consumer or business privacy, and what is shared in many cases is anonymized in such way that the data loses its value. This project will create new capabilities for sharing detailed yet privacy-preserving information about security incidents that will substantially alter the data-sharing pipeline, both within and across organizations and accelerate the industry transition to AI-driven security workflows. Having better AI-driven cybersecurity tools will have an enormous impact in protecting critical infrastructure and networks across all sectors from cybers attacks. This project will take an interdisciplinary approach spanning AI/ML, security, privacy, networked systems, law, and policy. It will tackle the fundamental tradeoffs among privacy, utility, and efficiency along three key thrusts: (1) design and implement novel generative adversarial networks (GANs) by which an enterprise can model its network data to inform anomaly detection by others. This thrust will design and implement novel GANs and analyze their privacy implications and their utility for use by others to detect malicious network activity. (2) Design and implement new cryptographic protocols and systems workflows for efficiently comparing hypotheses (suspicious identifiers, such as domain names, IP subnets, and program hashes) across enterprises to inform policy deployments. (3) Develop new legal and policy analyses on the implications of sharing such synthetic data, ML models, and hypotheses. By addressing these three critical areas and engaging key stakeholders, the tools developed by this project stand a high probably of gaining adoption and having tremendous value to the country by improving cybersecurity.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.

NSF融合加速器支持以使用为灵感，以团队为基础，多学科的努力，解决国家重要性的挑战，并将在不久的将来产生对社会有价值的可交付成果。当今，针对企业网络的网络攻击对企业运营构成了巨大威胁。防御不断变化的威胁和正常用户流量既耗时又费力。为了应对这一挑战，许多行业正在努力采用人工智能和机器学习（AI/ML）模型来自动检测和响应安全事件。然而，在实践中，人工智能/机器学习支持的工作流存在两个障碍：(1)缺乏足够的数据来训练可靠的模型来检测新的攻击活动或对正常行为进行建模；(2)在短时间内对模型输出缺乏信心，导致假阳性（即阻止合法用户）和假阴性（即错过攻击）之间的不良权衡。理想情况下，共享数据将有助于解决这两个问题，然而，由于对消费者或企业隐私的担忧，这些信息很少被共享（如果有的话），而且在许多情况下，共享的信息是匿名的，因此数据会失去其价值。该项目将为共享有关安全事件的详细且保护隐私的信息创造新的功能，这将大大改变组织内部和跨组织的数据共享管道，并加速行业向人工智能驱动的安全工作流程的过渡。拥有更好的人工智能驱动的网络安全工具将对保护所有部门的关键基础设施和网络免受网络攻击产生巨大影响。该项目将采用跨学科的方法，涵盖人工智能/机器学习、安全、隐私、网络系统、法律和政策。它将沿着三个关键方向解决隐私、效用和效率之间的基本权衡：(1)设计和实现新的生成对抗网络（gan），通过该网络，企业可以对其网络数据进行建模，以通知其他人进行异常检测。该推力将设计和实现新颖的gan，并分析其隐私含义及其用于检测恶意网络活动的实用性。(2)设计和实现新的加密协议和系统工作流，以便跨企业有效地比较假设（可疑标识符，如域名、IP子网和程序哈希），以通知策略部署。(3)对共享这些合成数据、ML模型和假设的影响进行新的法律和政策分析。通过解决这三个关键领域并吸引关键利益相关者，该项目开发的工具很可能被采用，并通过改善网络安全对国家产生巨大价值。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Detecting stuffing of a user's credentials at her own accounts

• 发表时间: 2019-12
• 期刊: ArXiv
• 作者: K. Wang;M. Reiter

Pareto GAN: Extending the Representational Power of GANs to Heavy-Tailed Distributions

• 发表时间: 2021-01
• 作者: Todd P. Huster;Jeremy E. Cohen;Zinan Lin;Kevin S. Chan;Charles A. Kamhoua;Nandi O. Leslie;C. Chiang;Vyas Sekar

On the Privacy Properties of GAN-generated Samples

• DOI: 10.48550/arxiv.2206.01349
• 发表时间: 2022-06
• 作者: Zinan Lin;Vyas Sekar;G. Fanti

The Netivus Manifesto: making collaborative network management easier for the rest of us

Netivus 宣言：让我们其他人更轻松地进行协作网络管理

• 发表时间: 2021
• 期刊: ACM SIGCOMM Computer Communication Review
• 影响因子: 2.8
• 作者: Severini, Joseph;Mysore, Radhika Niranjan;Sekar, Vyas;Banerjee, Sujata;Reiter, Michael K.
• 通讯作者: Reiter, Michael K.

Using Amnesia to Detect Credential Database Breaches

• 发表时间: 2021
• 作者: K. Wang;M. Reiter