Coordinated cyber-physical attacks (CCPA) have been touted as a serious threat for several years, where "coordinated" means that attackers have complete knowledge of the physical plant and status, and sometimes can even create physical defects, to assist cyber attacks, and vice versa. In recent years, these attacks have crept from theory to reality, with attacks on vehicles, electrical grids, and industrial plants, which have the potential to cause destruction and even death outside of the digital world. CCPA raise a unique challenge with respect to cyber-physical systems (CPS) safety. Historically, technologies to defend cyber attacks and physical attacks are developed separately under different assumptions and models. For instance, cyber security technologies often require the complete profile of the physical dynamics and the observation of the system state, which may not be available when physical defects exist. Similarly, existing system control techniques may efficiently compensate for the physical damage, but under the assumption that the control software and the sensor data are not compromised. There is a lack of unified approaches against CCPA. With this observation, this project focuses on the development of unified models with coherent set of assumptions, supported by integrated technologies, upon which CCPA can be defended much more effectively. To establish theoretical foundations and engineering principles for resilient CPS architectures, this project will investigate unified models and platforms that represent the scientific understanding of resilient CPS against CCPA. Engineering of CPS will be addressed through the development and integration of complexity-reduced software architectures, along with their design principles, which lead to verifiable and certifiable architectures with higher level of system resilience. Technology of CPS will be addressed through the design of new attack detection, isolation, and recovery tools as well as timing and control techniques to ensure appropriate responses to CCPA. The proposed inherently interdisciplinary research will ensure predictable performance for resilient CPS, by leveraging the disciplinary advances in (i) the design and evaluation of robust fault-tolerant control systems yielding significantly enhanced levels of safety in highly unpredictable environments; (ii) the design and implementation of complexity reduction architecture yielding a significant reduction in the verification time from hours to seconds; (iii) the development of multi-rate sampled-data control and robust reachability-based attack detection techniques ensuring that the sensor data is reliable; and (iv) the development of cyber-physical co-adaptation that optimizes control performance and computation task scheduling to guarantee system safety and efficient recovery from CCPA. The target application of this project is unmanned aerial vehicles (UAVs). The research results will be evaluated in three different testbeds: UAV testbed, generic transportation model (GTM) aircraft, and power system virtual testbed (VTB). The technological advancement from this project will provide solutions for the safety and reliability issues faced by today's CPS and deliver dependable CPS that are applicable without sacrificing functionality or accessibility in complex and potentially hostile networked environment. The results of this project will be communicated in archival journal publications, conference venues and various workshops and lectures, and will be integrated at different academic levels.

Airborne networking, unlike the networking of fixed sensors, mobile devices, and slowly-moving vehicles, is very challenging because of the high mobility, stringent safety requirements, and uncertain airspace environment. Airborne networking is important because of the growing complexity of the National Airspace System with the integration of unmanned aerial vehicles (UAVs). This project develops an innovative new theoretical framework for cyber-physical systems (CPS) to enable airborne networking, which utilizes direct flight-to-to-flight communication for flexible information sharing, safe maneuvering, and coordination of time-critical missions. This project uses an innovative co-design approach that exploits the mutual benefits of networking and decentralized mobility control in an uncertain heterogeneous environment. The approach departs from the usual perspective that views physical mobility as communication constraints, communication as constraints for decentralized mobility control, and uncertain environment as constraints for both. Instead, approach taken here proactively exploits the constraints, uncertainty, and new structures with information to enable high-performance designs. The features of the co-design such as scalability, fast response, trackability, and robustness to uncertainty advance the core CPS science on decision-making for large-scale networks under uncertainty. The technological advances developed in this research will contribute to multiple fields, including mobile networking, decentralized control, experiment design, and general real-time decision making under uncertainty for CPS. Technology transfer will be pursued through close collaboration with industries and national laboratories. This novel research direction will also serve as a unique backdrop to inspire the CPS workforce. New teaching materials will benefit the future CPS workforce by equipping them with a knowledge base in networking and control. Broad outreach and dissemination activities that involve undergraduate student societies, K-12 school teaching, and public events, all stemming from the PI's current efforts, will be enhanced.

Most critical infrastructures have evolved into complex systems comprising large numbers of interacting elements. These interactions result in the spread of disruptions, such as delays, from one part of the system to another, and even from one infrastructure to another. Effective tools for the analysis and control of real-world infrastructures need to account for the underlying dynamics. The key insight in this research is that by learning data-driven models of infrastructure networks, and using these models to determine dynamics-aware recovery algorithms, we can greatly improve the resilience of critical infrastructure networks. We propose to address these challenges by: 1. Learning and validating scalable representations of real systems from data. By considering continuous states, and by modeling the time-varying nature of connectivity as switching between network topologies, we propose to obtain a class of switched linear system models. Multilayer network models will be developed to account for airline networks, and multimodal systems. 2. Characterizing resilience, both for the system as a whole, and in terms of individual nodes (e.g., susceptibility to network delays). The metrics to evaluate resilience will encompass both steady-state and transient behavior. 3. Using the identified models to design optimal control algorithms that can enable recovery from disruptions, taking into account network dynamics, the uncertainty in operating environments, and the costs of decisions to restore service at various levels, at various times. The results of the research will be validated using operational data, thereby yielding a set of tools for system diagnostics, analysis, and recovery. Improving and maintaining critical infrastructures are among the grand challenges identified by the National Academy of Engineering. The proposed research will develop techniques grounded in network science, machine learning, and systems and control theory in order to effectively design and operate infrastructures. The development of common frameworks and abstractions for these infrastructures will enable the study of their interdependencies. With the rapid growth of intelligent infrastructures, the proposed research will benefit society, and also help attract and train the next generation of engineering professionals.

Equipment operation represents one of the most dangerous tasks on a construction sites and accidents related to such operation often result in death and property damage on the construction site and the surrounding area. Such accidents can also cause considerable delays and disruption, and negatively impact the efficiency of operations. This award will conduct research to improve the safety and efficiency of cranes by integrating advances in robotics, computer vision, and construction management. It will create tools for quick and easy planning of crane operations and incorporate them into a safe and efficient system that can monitor a crane's environment and provide control feedback to the crane and the operator. Resulting gains in safety and efficiency wil reduce fatal and non-fatal crane accidents. Partnerships with industry will also ensure that these advances have a positive impact on construction practice, and can be extended broadly to smart infrastructure, intelligent manufacturing, surveillance, traffic monitoring, and other application areas. The research will involve undergraduates and includes outreach to K-12 students. The work is driven by the hypothesis that the monitoring and control of cranes can be performed autonomously using robotics and computer vision algorithms, and that detailed and continuous monitoring and control feedback can lead to improved planning and simulation of equipment operations. It will particularly focus on developing methods for (a) planning construction operations while accounting for safety hazards through simulation; (b) estimating and providing analytics on the state of the equipment; (c) monitoring equipment surrounding the crane operating environment, including detection of safety hazards, and proximity analysis to dynamic resources including materials, equipment, and workers; (d) controlling crane stability in real-time; and (e) providing feedback to the user and equipment operators in a "transparent cockpit" using visual and haptic cues. It will address the underlying research challenges by improving the efficiency and reliability of planning through failure effects analysis and creating methods for contact state estimation and equilibrium analysis; improving monitoring through model-driven and real-time 3D reconstruction techniques, context-driven object recognition, and forecasting motion trajectories of objects; enhancing reliability of control through dynamic crane models, measures of instability, and algorithms for finding optimal controls; and, finally, improving efficiency of feedback loops through methods for providing visual and haptic cues.

As evidenced by the recent cyberattacks against Ukrainian power grids, attack strategies have advanced and new malware agents will continue to emerge. The current measures to audit the critical cyber assets of the electric power infrastructure do not provide a quantitative guidance that can be used to address security protection improvement. Investing in cybersecurity protection is often limited to compliance enforcement based on reliability standards. Auditors and investors must understand the implications of hypothetical worst case scenarios due to cyberattacks and how they could affect the power grids. This project aims to establish an actuarial framework for strategizing technological improvements of countermeasures against emerging cyberattacks on wide-area power networks. By establishing an actuarial framework to evaluate and manage cyber risks, this project will promote a self-sustaining ecosystem for the energy infrastructure, which will eventually help to improve overall social welfare. The advances in cyber insurance will stimulate actuarial research in handling extreme cyber events. In addition, the research and practice related to cybersecurity and cyber insurance for the critical energy infrastructure will be promoted by educating the next generation of the workforce and disseminating the research results. The objective of this project is to develop an actuarial framework of risk management for power grid cybersecurity. It involves transformative research on using insurance as a cyber risk management instrument for contemporary power grids. The generation of comprehensive vulnerabilities and reliability-based knowledge from extracted security logs and cyber-induced reliability degradation analysis can enable the establishment of risk portfolios for electric utilities to improve their preparedness in protecting the power infrastructure against cyber threats. The major thrusts of this project are: 1) developing an approach to quantifying cyber risks in power grids and determining how mitigation schemes could affect the cascading consequences to widespread instability; 2) studying comprehensively how hypothesized cyberattack scenarios would impact the grid reliability by performing a probabilistic cyber risk assessment; and 3) using the findings from the first two thrusts to construct actuarial models. Potential cyberattack-induced losses on electric utilities will be assessed, based on which insurance policies will be designed and the associated capital market will be explored.