Interconnections in modern systems make them vulnerable to adversarial attackers both by corrupting communication channels and compromising entire subsystems. The field of secure state estimation (SSE) aims to provide correct state estimation even when an unknown part of the measurement signals is corrupted. In this letter, we propose a solution to a novel generalized SSE problem in which full subsystems can be compromised, corrupting both the actuation and measurement signals. For a full system with p measurements, the proposed sliding mode observer (SMO)-based solution allows for up to p attack channels which can be arbitrarily distributed amongst attacks on actuation and measurement signals. This is a much larger class of attacks than considered in the existing literature. The method is demonstrated on 10 interconnected mass-spring-damper subsystems.

In this paper, we address the problem of distributed reconfiguration of networked control systems upon the removal of misbehaving sensors and actuators. In particular, we consider systems with redundant sensors and actuators cooperating to recover from faults. Reconfiguration is performed while minimizing a steady-state estimation error covariance and a quadratic control cost. A model-matching condition is imposed on the reconfiguration scheme. It is shown that the reconfiguration and its underlying computation can be distributed. Using an average dwell-time approach, the stability of the distributed reconfiguration scheme under finite-time termination is analyzed. The approach is illustrated in a numerical example.

Energy management systems (EMS) are used to control energy usage in buildings and campuses, by employing technologies such as supervisory control and data acquisition (SCADA) and building management systems (BMS), in order to provide reliable energy supply and maximise user comfort while minimising energy usage. Historically, EMS systems were installed when potential security threats were only physical. Nowadays, EMS systems are connected to the building network and as a result directly to the outside world. This extends the attack surface to potential sophisticated cyber-attacks, which adversely impact EMS operation, resulting in service interruption and downstream financial implications. Currently, the security systems that detect attacks operate independently to those which deploy resiliency policies and use very basic methods. We propose a novel EMS cyber-physical-security framework that executes a resilient policy whenever an attack is detected using security analytics. In this framework, both the resilient policy and the security analytics are driven by EMS data, where the physical correlations between the data-points are identified to detect outliers and then the control loop is closed using an estimated value in place of the outlier. The framework has been tested using a reduced order model of a real EMS site.