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

提出的研究发展的理论和技术，监测和诊断故障，危害，或更一般地说，功能变化的动态系统和网络，在有限的和可能损坏的信息。我们提出了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)通过分布式信念传播算法进行故障诊断，以及（vi）模型独立诊断。该研究团队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、信念传播和模型简化等领域的专业知识，在动态发展的环境中检测、识别和定位故障和异常。除了知识价值之外，提出的研究计划将在各种方面产生更广泛的影响。更广泛的影响：网络和网络化系统正日益巩固其作为国家经济和社会基础基石的作用。许多新兴的商业、政府、医疗、军事和安全应用在很大程度上依赖于这些系统，因此越来越需要确保这些关键基础设施在遭受恶意或非恶意破坏的情况下是可靠和值得信赖的。为了开发网络系统的全部潜力，使用现成的组件和软件构建值得信赖的网络系统提出了一个需要克服的重大障碍。拟议的项目概述了一种协同和综合的方法，用于在不确定信息和可能存在通信错误和约束的情况下诊断复杂系统和网络中的故障、对抗行为和威胁的可扩展方法。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实质性和及时的贡献，因为它在网络系统、通信网络和复杂数字系统的监测、测试和可靠安全操作方面具有重要意义。用于故障诊断的分布式算法的发展，以及由此产生的分布式系统的整体增强，使它们更加可靠，也有助于国家先进科学与工程优先领域（ASE）。数据模型、分布式算法、系统动力学、控制和决策的集成与数据、模型和通信（dmc）的技术重点领域保持一致，复杂动态系统和网络可靠运行的关键支持机制的开发与计算集成（int）的技术重点领域保持一致。智力优势：该建议的智力优势在于在广泛的故障检测和识别领域内对不同维度进行协同和全面的探索，或者更一般地说，复杂动态系统和网络中的异常行为。最终目标是开发适当的模型和创新的分布式算法，集成和统一来自许多不同学科的技术，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。除了推进各种诊断方法的前沿，总体主题是将这些思想整合到一个定义良好的方法中，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泛领域的同时，本研究将同时利用这些技术的适用性来设计测试平台，用于实验自组织移动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。教育影响和推广：该计划的主要教育目标是双重的：(i)开发课程和教育材料，讨论复杂系统和网络中故障诊断和容错的算法和架构的系统方法。例如，将为该领域的实践工程师建立关于特殊主题的网络讲座；将开设关于这一主题的高级/研究生课程；伊利诺斯大学将建立一个中央网页，在团队成员内部以及更广泛的科学和研究界传播新的成果。（ii）在我们各自的研究项目中继续积极招募和指导来自代表性不足群体的参与者

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