Towards Provable Security of Real-world Servers: Where Online Learning Meets Server Retrofitting

实现现实服务器的可证明安全性：在线学习与服务器改造的结合

• 批准号: 2140175
• 负责人: Minghui Zhu
• 金额: $ 42.5万
• 依托单位: Pennsylvania State Univ University Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2140175&HistoricalAwards=false
• 关键词: Towards; Provable; Security; Real; world

Servers located in enterprises (e.g. private data centers and public cloud data centers) play a critical role in human society. However, real-world servers are plagued by various security vulnerabilities. Memory overwrite and over-read vulnerabilities are among the most dangerous of the known vulnerabilities. They are the root causes for a variety of serious real-world server attacks. Cyber-defenses are broadly deployed to protect real-world servers from these cyberattacks. However, it is widely recognized in the cybersecurity community that there is no silver bullet. Moreover, the existing cyber-defenses (e.g. patching) are still very limited in handling the so-called zero-day vulnerabilities. Furthermore, a fundamental limitation is that the widely deployed real-world defenses usually do not provide provable guarantees. This project aims to develop online learning-based adaptive cyber defenses, which are expected to be able to provide provable guarantees for real-world servers. The developed defenses will present adversaries with optimized dynamically changing attack surfaces, thereby significantly increasing uncertainty and complexity that adversaries would need to overcome in order to succeed. These measures are expected to substantially improve adaptive and autonomous defense capabilities of real-world servers against zero-day attacks.This project will develop a new co-design framework to protect data centers against (i) stochastic attacks through dynamic runtime environments; (ii) intelligent strategic attacks through dynamic platforms; and (iii) multi-stage attacks through dynamic networks. The co-design framework will involve three intertwined components: newly synthesized mathematical models, online learning-based defense algorithms and server retrofitting. In particular, the mathematical models will be of high-fidelity and also analytically tractable to allow online learning to provide provable guarantees. On the other hand, the deviations of the mathematical models from real-world servers will be bridged by server retrofitting. In each proposed mathematical model, a utility function can be easily evaluated by deployed preliminary defenses and will provide necessary feedback to perform online learning, and on the other hand, it properly reflects the cost-effectiveness of defenses. Online learning algorithms are developed to tackle the unique challenges of computer security (e.g., detection delays, detection inaccuracies, strategic attacks, unknown system states and unknown exploit likelihoods). The most suitable server retrofitting will be customized to meet the assumptions of the mathematical models. Further, the three intertwined components will be integrated into real defenses. The proposed research is interdisciplinary and integrates technical tools from machine learning, game theory, control theory and cybersecurity. Hackathon events will be held to inspire students’ engagement in research on machine learning and cybersecurity. All the research results will be made available to industrial stakeholders, federal government agencies and the research community.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.

位于企业中的服务器（例如私有数据中心和公共云数据中心）在人类社会中发挥着至关重要的作用，然而，现实世界的服务器受到各种安全漏洞的困扰，其中内存覆盖和过度读取漏洞是最危险的。它们是各种严重的现实世界服务器攻击的根本原因，人们广泛部署网络防御来保护现实世界的服务器免受这些网络攻击。此外，现有的网络防御（例如修补）在处理所谓的零日漏洞方面仍然非常有限。此外，一个根本的限制是广泛部署的现实世界防御通常无法提供可证明的保证。该项目旨在开发基于在线学习的自适应网络防御，预计将为现实世界的服务器提供可证明的保证，从而为对手提供优化的动态变化的攻击面，从而增加不确定性。这些措施预计将大大提高现实世界服务器抵御零日攻击的自适应和自主防御能力。该项目将开发一种新的协同设计框架来保护数据中心。对抗（i）通过动态运行环境的随机攻击；（ii）通过动态平台的战略智能攻击；以及（iii）通过动态网络的多阶段攻击。协同设计框架将涉及三个相互交织的组件：新合成的数学模型、在线攻击。基于学习的防御算法和服务器特别是，数学模型将具有高保真度并且易于分析，以便在线学习提供可证明的保证。另一方面，数学模型与现实世界服务器的偏差将通过服务器改造来弥补。在每个提出的数学模型中，可以通过部署的初步防御轻松评估效用函数，并提供必要的反馈来反映在线学习的执行情况，另一方面，它可以适当地开发在线学习算法来解决问题。独特的挑战将定制最合适的服务器改造以满足数学模型的假设。拟议的研究是跨学科的，整合了机器学习、博弈论、控制理论和网络安全的技术工具，将举办黑客马拉松活动，以激发学生参与机器学习和网络安全的研究。可用的该奖项反映了 NSF 的法定使命，并通过使用基金会的智力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

Byzantine-tolerant federated Gaussian process regression for streaming data

• 发表时间: 2022
• 作者: Xu Zhang;Zhenyuan Yuan;Minghui Zhu

Efficient Gradient Approximation Method for Constrained Bilevel Optimization

• DOI: 10.1609/aaai.v37i10.26473
• 发表时间: 2023-02
• 作者: Siyuan Xu;Minghui Zhu

Detecting Vulnerabilities in Linux-Based Embedded Firmware with SSE-Based On-Demand Alias Analysis

• DOI: 10.1145/3597926.3598062
• 发表时间: 2023-07
• 期刊: Proceedings of the 32nd ACM SIGSOFT International Symposium on Software Testing and Analysis
• 作者: Kai Cheng;Yaowen Zheng;Tao Liu;Le Guan;Peng Liu;Hong Li;Hongsong Zhu;Kejiang Ye;Limin Sun