Large-scale networked systems (such as the power grid, the internet, multi-robot systems, and smart cities) consist of a large number of interconnected components. To allow the entire system to function efficiently, these components must communicate with each other and use the exchanged information in order to estimate the state of the entire system and take optimal actions. However, such large-scale networked systems are also increasingly under threat from sophisticated cyber-attacks that can compromise some of the components and cause them to behave erratically or inject malicious information into the network. Existing algorithms for distributed coordination in large-scale networks are highly vulnerable to such attacks. This project will address this critical problem by creating new algorithms to enable components in large-scale networks to cooperatively take optimal actions and estimate the state of the system despite attacks on a large number of the components. The algorithms will provide provable security and performance guarantees, and identify characteristics of networks and algorithms that are vulnerable to attacks. The project will identify new ways to design networks that provide a desired level of resilience to attacks. The algorithms that arise from the research will enable the design of more secure networks and critical infrastructure that remain functional under attacks, with substantial benefits to society. In addition to the technical and scientific contributions, the project will also train students in the design of secure networked systems, and will engage the local community in central Indiana in learning about networks via interactive exhibits and workshops at the local museum. This proposal presents an integrated research and education program focused on establishing the foundations of distributed optimization, learning, and estimation algorithms that are resilient to attacks. The research agenda is focused along three thrusts: (i) designing resilient algorithms for distributed optimization of static objective functions, (ii) designing resilient learning algorithms for settings where optimization objectives change over time, and (iii) designing resilient distributed state estimators for large scale dynamical systems. The three research thrusts each lead to new theoretical contributions. First, the proposed research will establish new metrics for measuring resilience in distributed optimization algorithms, and will build upon commonly studied optimization approaches (which are highly vulnerable to adversaries in their existing forms) to derive resilient distributed optimization algorithms. Second, it will establish new fundamental lower bounds on the regret that can be achieved with distributed online learning algorithms under adversarial behavior, and characterize achievable regret bounds via the design of new learning algorithms. Third, the proposed research will investigate the interplay between the dynamics of underlying physical systems and the communication network topology between distributed observers in order to design resilient distributed state estimation schemes. The proposed research will lead to a greater understanding of the fundamental factors that affect the resilience of distributed optimization, learning, and estimation dynamics, and establish systematic procedures to design large-scale networked systems that are capable of operating in a near-optimal manner under attacks. Given the lack of existing work on this topic, the research will lay the groundwork for substantial further explorations of resilient algorithms for distributed decision-making and coordination in large-scale networks.