False Data Injection Detection in Cyber-Physical System

Abstract

The interconnectedness of the CPS allows sharing data with relevant systems and taking more informed process control decisions; however, it also broadens the attack surface. One pain point of IoT, technology usually applied in CPS, is that embedded devices are usually low energy and do not have enough processing power to operate while keeping up with security measures, such as encryption and data validation. As a result, attackers may use such devices to launch attacks on the network, compromising the whole CPS infrastructure. One common type of attack is the False Data Injection (FDI), in which the attacker has access to a communication channel and can change the value read by a sensor or sent to an actuator. One way of coping with such attacks is to develop a bank of observer by using Functional Observers, which are better suited for high-dimensional, sparse systems. It does not suffer from sparsity\’s numerical problems and uses a reduced-order system to observe only the desired states, significantly reducing the computational burden of the observer. We propose an LMI-based approach to design a bank of residual generators for functional observers to detect such attacks. This approach has the advantage of using a reduced order arbitrary dynamic system, making it suitable for large-scale smart grids, and the use of LMI, allowing the easy insertion of restrictions