Collaborative Research: CCRI: New: Medium: A Development and Experimental Environment for Privacy-preserving and Secure (DEEPSECURE) Machine Learning

合作研究：CCRI：新：媒介：隐私保护和安全（DEEPSECURE）机器学习的开发和实验环境

• 批准号: 2120369
• 负责人: Chunming Qiao
• 金额: $ 52万
• 依托单位: SUNY at Buffalo
• 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: 2021-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2120369&HistoricalAwards=false
• 关键词: Collaborative; Research; CCRI; New; Medium

While machine learning (ML) is embraced as an important tool for various science, engineering, medical, finance, and homeland security applications, it is becoming an increasingly attractive target for cybercriminals. DEEPSECURE is a first-of-its-kind development and experimental platform to support secure and privacy-preserving ML research. With its novel modular design integrated with fully customizable function blocks and sample modules, DEEPSECURE is a game-changing tool to effectively support research in this emerging field by enabling fast design, prototyping, evaluation, and re-innovation of trust-worthy ML applications. It enables a variety of compelling new research projects that focus on ML security and privacy, leading to breakthroughs to protect ML systems and accelerating their development and widening their adoption. It will contribute significantly to the protection of the future cyber and physical world and safeguard human society. DEEPSECURE receives strong community support from over 20 key stakeholders across the country. The project includes significant efforts for fostering and sustaining an ML security and privacy research community, including monthly virtual open forums to provide a regular update to and seek feedback from the community, quarterly advisory board meetings, annual symposiums, and a training workshop series. The project includes specific measures and plans for inspiring the participation of underrepresented groups and infusing diversity and inclusion in all DEEPSECURE events and activities. The project output includes an open-source and easy-to-use learning platform for curriculum development and workforce training. To support building a sustainable workforce development pipeline, the project team participates in the existing annual GenCyber summer camps for K-12 students and a Cyber Saturday series to introduce cybersecurity and AI career paths and educational resources to K-12 school counselors, teachers, students, and parents.Recent development in privacy-preserving and secure ML draws expertise from both ML and security/privacy to tackle the multi-faceted problem. However, the research community is facing fundamental challenges in this emerging area due to its interdisciplinary nature. On the one hand, although deep learning frameworks such as Pytorch and Tensorflow have been made widely available, a critical hurdle faced by ML researchers is the steep learning curve to effectively use security techniques and libraries to tackle ML security and privacy problems. On the other hand, while the security community has developed highly efficient cryptographic libraries, it remains nontrivial to integrate them into deep learning models to achieve a computation efficiency suited for practical applications. The overarching goal of the project is to close the gap by developing DEEPSECURE, which integrates a spectrum of essential functions and building blocks that are ready-to-use to flatten the learning curve for researchers coming from both ML and security/privacy communities. At the same time, DEEPSECURE is fully customizable and scalable, enabling deep and fundamental research toward privacy-preserving and secure ML. To meet the overarching goal, specific project objectives include: (1) acquiring a scalable and re-configurable compute environment based on the latest Dell, AMD, and Nvidia technologies to establish the DEEPSECURE hardware infrastructure across the campuses of Old Dominion University and University of Buffalo; (2) developing a new software platform to support DEEPSECURE SDE (Software Development Environment) and MEC (Multi-user Experimental Chamber). The platform is integrated with PyTorch to enable great usability for both beginners and advanced researchers and feature a scalable and customizable modular framework with seamlessly integrated libraries, function blocks, and sample modules; (3) promoting DEEPSECURE across the nation to ensure broad participation, collaboration, and sharing; (4) leveraging DEEPSECURE to foster a long-lasting, self-sustainable ML security and privacy research community that engages all stakeholders in a sustained and ongoing way; and last but not least, (5) educating and training diverse cybersecurity workforce to safeguard the future intelligent cyber systems.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.

虽然机器学习（ML）被视为各种科学、工程、医疗、金融和国土安全应用的重要工具，但它正成为网络犯罪分子越来越有吸引力的目标。DEEPSECURE是首个支持安全和隐私保护ML研究的开发和实验平台。凭借其新颖的模块化设计，集成了完全可定制的功能块和示例模块，DEEPSECURE是一个改变游戏规则的工具，通过实现可信赖的ML应用程序的快速设计，原型设计，评估和再创新，有效地支持这一新兴领域的研究。它使各种引人注目的新研究项目成为可能，这些项目专注于ML安全和隐私，从而在保护ML系统方面取得突破，加速其开发并扩大其采用。它将为保护未来的网络和物理世界，维护人类社会做出重大贡献。DEEPSECURE得到了全国20多个主要利益相关者的强有力的社区支持。该项目包括培养和维持ML安全和隐私研究社区的重大努力，包括每月虚拟开放论坛，以提供定期更新并寻求社区反馈，季度咨询委员会会议，年度研讨会和培训研讨会系列。该项目包括具体措施和计划，以鼓励代表性不足的群体参与，并在所有DEEPSECURE活动中注入多样性和包容性。该项目的产出包括一个用于课程编制和劳动力培训的开放源码和易于使用的学习平台。为了支持建立可持续的劳动力发展管道，项目团队参加了现有的K-12学生年度GenCyber夏令营和Cyber Saturday系列，向K-12学校辅导员，教师，学生，隐私保护和安全ML的最新发展吸引了ML和安全/隐私的专业知识来解决多方面的问题。然而，由于其跨学科性质，研究界在这一新兴领域面临着根本性的挑战。 一方面，尽管Pytorch和Tensorflow等深度学习框架已经广泛使用，但ML研究人员面临的一个关键障碍是有效使用安全技术和库来解决ML安全和隐私问题的陡峭学习曲线。另一方面，虽然安全社区已经开发了高效的加密库，但将它们集成到深度学习模型中以实现适合实际应用的计算效率仍然是不平凡的。该项目的总体目标是通过开发DEEPSECURE来缩小差距，DEEPSECURE集成了一系列基本功能和构建块，这些功能和构建块可以随时使用，以使来自ML和安全/隐私社区的研究人员的学习曲线变平。与此同时，DEEPSECURE是完全可定制和可扩展的，可以对隐私保护和安全ML进行深入的基础研究。为了实现总体目标，具体的项目目标包括：（1）获得基于最新的戴尔、AMD和Nvidia技术的可扩展和可重新配置的计算环境，以在Old自治领大学和布法罗大学的校园内建立DEEPSECURE硬件基础架构;（2）开发了支持DEEPSECURE软件开发环境（Software Development Environment）和多用户实验室（Multi-user Experimental Chamber）的软件平台。该平台与PyTorch集成，为初学者和高级研究人员提供了很好的可用性，并具有可扩展和可定制的模块化框架，无缝集成了库，功能块和示例模块;（3）在全国范围内推广DEEPSECURE，以确保广泛的参与，协作和共享;（4）利用DEEPSECURE来培育一个持久的、自我可持续的ML安全和隐私研究社区，以持续和持续的方式吸引所有利益相关者;最后但并非最不重要的是，（5）教育和培训多样化的网络安全人员，以保护未来的智能网络系统。该奖项反映了NSF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

Can We Use Arbitrary Objects to Attack LiDAR Perception in Autonomous Driving?

• DOI: 10.1145/3460120.3485377
• 发表时间: 2021-11
• 期刊: Proceedings of the 2021 ACM SIGSAC Conference on Computer and Communications Security
• 作者: Yi Zhu;Chenglin Miao;T. Zheng;Foad Hajiaghajani;Lu Su;Chunming Qiao

Understanding and Measuring Robustness of Vision and Language Multimodal Models

理解和测量视觉和语言多模态模型的鲁棒性

• 发表时间: 2023
• 期刊: Proceedings of the International Conference on Secure Knowledge Management (SKM 2023
• 作者: Vishwamitra, Nishant;Guo, Keyan;Hu, Hongxin;Zhao, Ziming;Cheng, Long;Luo, Feng
• 通讯作者: Luo, Feng

BYOZ: Protecting BYOD Through Zero Trust Network Security

BYOZ：通过零信任网络安全保护 BYOD

• 发表时间: 2022
• 期刊: and Storage
• 作者: Anderson, John;Huang, Qiqing;Cheng, Long;Hu, Hongxin
• 通讯作者: Hu, Hongxin

xNIDS: Explaining Deep Learning-based Network Intrusion Detection Systems for Active Intrusion Responses

• 发表时间: 2023
• 期刊: Proceedings of the 36th Annual Computer Security Applications Conference
• 作者: Feng Wei;Hongda Li;Ziming Zhao;Hongxin Hu

SysFlow: Toward a Programmable Zero Trust Framework for System Security

• DOI: 10.1109/tifs.2023.3264152
• 发表时间: 2023
• 期刊: IEEE Transactions on Information Forensics and Security
• 影响因子: 6.8
• 作者: Sungmin Hong;Lei Xu;Jianwei Huang;Hongda Li;Hongxin Hu;G. Gu