CAREER:Formal Synthesis of Provably Correct Cyber-Physical Defense with Asymmetric Information

职业：利用不对称信息对可证明正确的网络物理防御进行正式综合

• 批准号: 2144113
• 负责人: Jie Fu
• 金额: $ 54.82万
• 依托单位: University of Florida
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-06-01 至 2027-05-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2144113&HistoricalAwards=false
• 关键词: CAREER; Formal; Synthesis; Provably; Correct

For mission-critical Cyber-physical systems (CPSs), it is crucial to ensure these systems behave correctly while interacting with open, dynamic, and uncertain environments. Synthesizing CPSs with assurance is a daunting task: On the one hand, the interconnected networks, sensors, and (semi-) autonomous systems introduce unprecedented vulnerabilities to both cyber- and physical spaces; On the other hand, purposeful attacks may aim to compromise more complex system properties beyond traditional stability and safety. For example, in a robotized security patrol system, a successful cyber-attack on the sensor network can be combined with adversarial control commands to compromise the system and disrupt its mission. In this project, the goal is to develop intelligent sensing and control methods for CPSs that leverage advanced cyber defense techniques for constructing provably secured systems subject to high-level complex mission objectives.To achieve this goal, this project will develop formal modeling and solutions for a class of CPS Games, featured by multi-stage, strategic interactions between a controller/defender and a (coordinated cyber- and physical-) attacker. The synthesis of CPS control/defense strategies will explicitly account for asymmetric information and investigate how to leverage cyber defense and deception for guaranteed performance in mission-critical CPSs. The fundamental theory and algorithms will be validated via both a physical testbed including multiple mobile robots and a wireless sensor network and a simulation as a virtual proving ground. The technical contributions are: 1) to develop solutions of imperfect information games with temporal logic objectives and employ the solutions to design control and information acquisition strategies for CPSs under attacks; 2) to investigate the use of cyber defense techniques for gaining defender information advantages against CPS attackers in the synthesis of integrated cyber-physical defense; 3) to develop control strategies against coordinated cyber- and physical- attacks, and novel control methods that incorporate human’s preference for CPS resilience against prior unknown attacks. The project also includes an educational plan by developing an interdisciplinary curriculum for engineering students on cyber-physical security and attack mitigation techniques. The PI will promote K-12 outreach by participating in campus-wide activities and offering a two-week summer course for high-school students with hands-on robotic and wireless sensor networks experiments, aiming to raise students’ interest in science and engineering and public’s awareness of CPS security.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.

对于关键任务网络物理系统（cps），确保这些系统在与开放、动态和不确定环境交互时正确运行至关重要。综合保证cps是一项艰巨的任务：一方面，互联网络、传感器和（半）自主系统给网络和物理空间带来了前所未有的漏洞；另一方面，有目的的攻击可能旨在破坏传统稳定性和安全性之外的更复杂的系统属性。例如，在机器人安全巡逻系统中，对传感器网络的成功网络攻击可以与对抗性控制命令相结合，从而破坏系统并破坏其任务。在这个项目中，目标是为cps开发智能传感和控制方法，利用先进的网络防御技术来构建可证明的安全系统，以应对高水平复杂的任务目标。为了实现这一目标，该项目将为一类CPS游戏开发正式的建模和解决方案，其特点是控制器/防御者与（协调的网络和物理）攻击者之间的多阶段战略交互。CPS控制/防御策略的综合将明确考虑信息不对称，并研究如何利用网络防御和欺骗来保证关键任务CPS的性能。基本理论和算法将通过包括多个移动机器人和无线传感器网络在内的物理测试平台以及作为虚拟试验场的模拟来验证。技术上的贡献有：1)提出了具有时间逻辑目标的不完全信息博弈的解决方案，并利用该解决方案设计了攻击下cps的控制和信息获取策略；2)研究在综合网络物理防御中，利用网络防御技术获取防御方信息优势以对抗CPS攻击者；3)制定针对协同网络和物理攻击的控制策略，以及结合人类对CPS抵御先前未知攻击的偏好的新型控制方法。该项目还包括一项教育计划，为工程专业学生开发一门关于网络物理安全和攻击缓解技术的跨学科课程。为了提高学生们的理工科兴趣和公众对CPS安全的意识，PI计划通过参与全校范围内的活动，为高中学生提供为期两周的机器人和无线传感器网络实验的暑期课程，促进K-12的外展。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

Optimal Resource Allocation for Proactive Defense with Deception in Probabilistic Attack Graphs

概率攻击图中欺骗主动防御的最优资源分配

• 发表时间: 2023
• 期刊: Decision and Game Theory for Security
• 作者: Ma, Haoxiang;Han, Shuo;Kamhoua, Charles;Fu, Jie
• 通讯作者: Fu, Jie

Synthesis of Opacity-Enforcing Winning Strategies Against Colluded Opponent

• DOI: 10.1109/cdc49753.2023.10384109
• 发表时间: 2023-04
• 期刊: 2023 62nd IEEE Conference on Decision and Control (CDC)
• 作者: Chongyang Shi;A. Kulkarni;Hazhar Rahmani;Jie Fu

Opportunistic Qualitative Planning in Stochastic Systems with Incomplete Preferences over Reachability Objectives

• DOI: 10.23919/acc55779.2023.10156127
• 发表时间: 2022-10
• 期刊: 2023 American Control Conference (ACC)
• 作者: A. Kulkarni;Jie Fu

Probabilistic Planning with Prioritized Preferences over Temporal Logic Objectives

• DOI: 10.48550/arxiv.2304.11641
• 发表时间: 2023-04
• 作者: Lening Li;Hazhar Rahmani;Jie Fu

Probabilistic Planning with Partially Ordered Preferences over Temporal Goals

• DOI: 10.1109/icra48891.2023.10160678
• 发表时间: 2022-09
• 期刊: 2023 IEEE International Conference on Robotics and Automation (ICRA)
• 作者: Hazhar Rahmani;A. Kulkarni;Jie Fu