ITR: COLLABORATIVE RESEARCH: -(NHS+ASE)-(dmc+int): Diagnosis and Assessment of Faults, Misbehavior and Threats in Distributed Systems and Networks

这项拟议的研究开发了在有限和可能被破坏的信息下，监测和诊断动态系统和网络中的故障、危险或更一般的功能变化的理论和技术。我们提出了一个统一和多方面的方法来解决这个问题，将大量的故障诊断研究分解为六个主题：(I)确定性故障诊断，(Ii)基于模型的概率诊断，(Iii)自适应和顺序诊断，(Iv)有线/无线网络中具有通信约束的分布式系统级诊断，(V)基于分布式信任传播算法的故障诊断，以及(Vi)模型无关诊断。研究小组将利用其在故障诊断、顺序检测、系统级诊断、分布式控制、建模、分析和性能评估、应用概率、图论、信任传播和模型简化等领域的专业知识，来解决在动态变化的环境中检测、识别和定位故障和异常的问题。除了智力价值，这项研究计划将在各种方面产生更广泛的影响。更广泛的影响：网络和联网系统正在日益巩固它们作为国家经济和社会基础的基石的角色。许多新兴的商业、政府、医疗、军事和安全应用都严重依赖这些系统，因此越来越需要确保这些关键基础设施是可靠和值得信赖的，无论是恶意的还是非恶意的中断。使用现成的组件和软件构建值得信赖的联网系统是一个需要克服的重大障碍，以便充分发挥联网系统的潜力。拟议的项目概述了在信息不确定的情况下，以及在可能存在通信错误和限制的情况下，诊断复杂系统和网络中的故障、敌对行为和威胁的可伸缩方法的协同和综合方法。该项目的成功完成将对国家和国土安全(NHS)的国家优先领域做出重大和及时的贡献，因为它在网络系统、通信网络和复杂的数字系统的监测、测试以及可靠和安全的运行方面产生了影响。用于故障诊断的分布式算法的开发，以及由此导致的分布式系统的全面增强，使其更加可靠，也为国家高级科学与工程优先领域(ASE)做出了贡献。数据模型、分布式算法、系统动力学、控制和决策的集成符合数据、模型和通信(DMC)的技术重点领域，而复杂动态系统和网络可靠运行的关键支持机制的开发符合计算集成(INT)的技术重点领域。智力优势：该建议的智力优点在于在检测和识别故障或更广泛地说，复杂动态系统和网络中的异常行为的广泛领域内协同和全面地探索不同维度。最终目标是开发合适的模型和创新的分布式算法，集成和统一来自多个不同学科的技术，包括离散事件系统中的故障诊断、检测和估计、图论和优化、分布式系统级诊断、信念传播、模型约简和信息论。除了推进各种个人诊断方法的前沿外，最重要的主题是将这些想法整合到一个定义明确的方法中，通过可扩展的模型和算法实现确定性和概率方法的优势。在扩展复杂动态系统和网络故障诊断的广阔领域的同时，本研究将利用这些技术的适用性来设计测试平台，以试验移动自组织网络中的分布式故障诊断和室内传感器网络中的故障定位。教育影响和扩展：该项目的主要教育目标有两个：(I)开发课程和教材，讨论复杂系统和网络中故障诊断和容错的算法和体系结构的系统方法。例如，将为该领域的实习工程师建立有关感兴趣专题的网上讲座；将开发关于这一专题的高级/研究生课程；将在伊利诺伊大学维护一个中心网页，以便在团队成员以及更广泛的科学和研究界传播新成果。(2)在我们各自的研究方案中，继续从代表性不足的群体中积极招募和指导参与者。

The proposed research develops theory and techniques for monitoring and diagnosing faults, hazards or, more gener-ally,functional changes in dynamic systems and networks, under limited and possibly corrupted information. We presenta unifying and multifaceted approach to this problem that decomposes the large body of fault diagnosis research into sixtopics: (i) deterministic fault diagnosis, (ii) model-based probabilistic diagnosis, (iii) adaptive and sequential diagnosis,(iv) distributed system-level diagnosis with communication constraints in wired/wireless networks, (v) fault diagnosisvia distributed belief propagation algorithms, and (vi) model-independent diagnosis. The research team will leverageits expertise in the areas of fault diagnosis, sequential detection, system-level diagnosis, distributed control, modeling,analysis and performance evaluation, applied probability, graph theory, belief propagation and model reduction to theproblem of detecting, identifying and localizing faults and abnormalities in dynamically evolving environments. Beyondintellectual value, the research program proposed will have broader impacts in a variety of ways.Broader Impact: Networks and networked systems are increasingly solidifying their roles as building blocks of thenation's economic and social foundation. Numerous emerging commercial, governmental, medical, military and securityapplications are vitally dependent on these systems, creating a growing need for ensuring that these critical infrastructuresare reliable and trustworthy in spite of malicious or non-malicious disruptions. Building trustworthy networked systemsusing off-the-shelf components and software presents a significant hurdle that needs to be overcome in order to exploit thefull potential of networked systems. The proposed project outlines a synergistic and comprehensive approach for scalablemethodologies for diagnosing faults, adversarial behavior and threats in complex systems and networks, under uncertaininformation and possibly in the presence of communication errors and constraints. The successful completion of thisproject will make a substantial and timely contribution to the National Priority Area of National and Homeland Security(NHS) because of its ramifications in the monitoring, testing, and reliable and secure operation of networked systems,communication networks, and complex digital systems. The development of distributed algorithms for fault diagnosisand the resulting overall enhancement of distributed systems in ways that make them more reliable also contributesto the National Priority Area of Advanced Science and Engineering (ASE). The integration of data models, distributedalgorithms, system dynamics, control and decision making is aligned with the technical focus area of data, models andcommunications (dmc), and the development of critical support mechanisms for reliable operation of complex dynamicsystems and networks is aligned with the technical focus area of integration of computing (int).Intellectual Merit: The intellectual merit of this proposal lies in the synergistic and comprehensive exploration of dif-ferentdimensions within the broad area of detection and identification of faults or, more generally, abnormal behavior incomplex dynamic systems and networks. The ultimate goal is to develop appropriate models and innovative distributedalgorithms that integrate and unify techniques from a number of diverse disciplines, including fault diagnosis in discreteevent systems, detection and estimation, graph theory and optimization, distributed system-level diagnosis, belief propa-gation,model reduction and information theory. Apart from advancing the forefront of the various individual approachesto diagnosis, the overarching theme is the integration of these ideas into a well-defined approach that achieves the ad-vantagesof both deterministic and probabilistic methodologies via scalable models and algorithms. While extending thefrontiers in the broad area of fault diagnosis in complex dynamic systems and networks, this research will at the sametime leverage the applicability of these techniques to the design of test platforms for experimenting with distributed faultdiagnosis in ad-hoc mobile networks and fault localization in indoor sensor networks.Educational Impact and Outreach: The main educational goals of this program are two-fold: (i) To develop coursesand educational materials that discuss systematic approaches for algorithms and architectures for fault diagnosis andtolerance in complex systems and networks. For example, Web-based lectures on special topics of interest will beestablished for practicing engineers in the field; a senior/graduate level course on this topic will be developed; a centralWeb-page will be maintained at the University of Illinois to disseminate new results within the members of the team aswell as to the broader scientific and research community. (ii) To continue to actively recruit and mentor participants fromunderrepresented groups in our respective research programs.1