CNS Core: Small: Collaborative Research: Scalable Penetration Test Generation for Automotive Systems

CNS 核心：小型：协作研究：汽车系统的可扩展渗透测试生成

• 批准号: 1908549
• 负责人: Sandip Ray
• 金额: $ 25.49万
• 依托单位: University of Florida
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1908549&HistoricalAwards=false
• 关键词: CNS; Core; Small; Collaborative; Research

This project develops a comprehensive framework for security validation of modern automotive systems. With increasing autonomy, automotive systems are evolving into very complex distributed systems. They contain more than a hundred electronic control units (ECU), a heterogeneous collection of sensors and actuators, several in-vehicle communication networks, and several hundred megabytes of software. Currently security validation of these systems depends primarily on human expertise to identify vulnerabilities in design and implementation. Clearly this does not scale to large complex systems. The project addresses this problem by introducing automated penetration testing methods capable of handling the exploding automotive system complexities. This project develops technology for systematic analysis of diverse safety, security, and reliability requirements in current and emergent vehicles. It enables early comprehension of conflict, trade-offs, and potential internal inconsistencies among the different requirements. The framework includes: (1) an adaptive virtual prototyping infrastructure that enables smooth integration of ECU, sensor, and actuator models; and (2) a concolic testing facility to generate penetration tests automatically for targeted adversary models. The analysis techniques developed in the research cross-cut hardware, software, and physical (sensory and actuarial) artifacts. The framework brings together currently disparate research in security, machine intelligence, and decision science. This project promises transformative technical and societal impacts through drastically improved safety, security, and reliability of diverse cyber-physical systems in general and automotive systems in particular. Research results will be integrated into graduate and undergraduate courses. A new workshop will be introduced to bring together experts in automotive safety, security, and reliability, and cross-cutting areas. Hands-on training modules for undergraduate and high school students will be developed using automotive simulator platforms. Participation of underrepresented students in the project will be actively encouraged. Industry connections will be used and actively pursued for technology transfer.All artifacts and data generated during the course of this project will be made publicly available, enabling the broader community to reproduce and extend research results. This includes all software and tools developed, architecture and platform models, and any experimental data supporting the research conclusions. A public repository has been set up at the URL https://github.com/RaySandip/AutoSec19.git for this dissemination, and will be maintained for at least three years beyond the award period. Backup copies of the data is expected to be retained indefinitely.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.

该项目为现代汽车系统的安全验证开发了一个综合框架。随着自主权的增加，汽车系统正在发展为非常复杂的分布式系统。 它们包含一百多个电子控制单元（ECU），一个异质的传感器和执行器集合，几个车载通信网络以及数百个兆字节的软件。 当前对这些系统的安全验证主要取决于人类专业知识，以确定设计和实施中的漏洞。 显然，这不是扩展到大型复杂系统。该项目通过引入能够处理爆炸汽车系统复杂性的自动渗透测试方法来解决此问题。该项目开发了技术，用于系统分析当前和新兴车辆的各种安全性，安全性和可靠性要求。 它可以尽早理解冲突，权衡以及不同要求之间潜在的内部矛盾。该框架包括：（1）自适应虚拟原型基础架构，该基础结构能够平稳地集成ECU，传感器和执行器模型； （2）一种共同测试设施，以自动为目标对手模型生成穿透性测试。在研究跨切割硬件，软件以及物理（感觉和精算）工件中开发的分析技术。 该框架汇集了当前在安全，机器智能和决策科学方面的不同研究。该项目通过尤其是一般和汽车系统的各种网络物理系统的安全性，安全性和可靠性大大提高了安全性，安全性和可靠性，从而有助于变革性的技术和社会影响。 研究结果将纳入研究生和本科课程。 将引入一个新的研讨会，以汇集汽车安全，安全性和可靠性以及横切区域的专家。将使用汽车模拟器平台开发针对本科和高中生的动手培训模块。 将积极鼓励代表人数不足的学生参与该项目。将使用行业联系并积极地进行技术传输。该项目过程中生成的所有工件和数据将公开可用，从而使更广泛的社区能够复制和扩展研究结果。这包括所有开发的软件和工具，建筑和平台模型以及支持研究结论的任何实验数据。已在URL https://github.com/raysandip/autosec19.git上设立了一个公共存储库，并将在颁奖典礼期间至少维持三年。该数据的备份副本预计将无限期保留。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子和更广泛影响的评论标准来评估值得支持的。

项目成果

Resilient Cooperative Adaptive Cruise Control for Autonomous Vehicles Using Machine Learning

• DOI: 10.1109/tits.2022.3144599
• 发表时间: 2021-03
• 期刊: IEEE Transactions on Intelligent Transportation Systems
• 影响因子: 8.5
• 作者: Srivalli Boddupalli;Akash Rao;S. Ray

Security of Multi-Agent Cyber-Physical Systems: A Survey

• DOI: 10.1109/access.2022.3223362
• 发表时间: 2022
• 期刊: IEEE Access
• 影响因子: 3.9
• 作者: Richard Owoputi;Sandip Ray

A Virtual Prototyping Platform for Exploration of Vehicular Electronics

• DOI: 10.1109/jiot.2023.3267339
• 发表时间: 2023-09
• 期刊: IEEE Internet of Things Journal
• 影响因子: 10.6
• 作者: Md Rafiul Kabir;Bhagawat Baanav Yedla Ravi;Sandip Ray

Security of Emergent Automotive Systems: A Tutorial Introduction and Perspectives on Practice

• DOI: 10.1109/mdat.2019.2944086
• 发表时间: 2019-09
• 期刊: IEEE Design & Test
• 影响因子: 2
• 作者: A. Lopez;A. Malawade;M. A. Al Faruque;Srivalli Boddupalli;S. Ray

Deep-Learning-Based Intrusion Detection for Autonomous Vehicle-Following Systems

基于深度学习的自主车辆跟随系统入侵检测

• 发表时间: 2021
• 期刊: 4th IEEE International Conference on Intelligent Transportation Systems - ITSC2021
• 作者: Wang, S;Lin, C;Boddupalli, S;Lin, C;Shih, C;Ray, S.
• 通讯作者: Ray, S.