Introducing distributed communication and control in DC microgrids significantly improves the overall performance of the system. However, these developments also raise the possibility of malicious attacks. Denial-of-service (DoS) attacks and false data injection (FDI) attacks are the most common cyber-attacks. DoS attacks disrupt data availability, and FDI attacks tamper with data integrity, which is perilous for the DC microgrid system. These attacks have the potential to create a detrimental effect on the DC microgrid's sensors, actuators, and communication channels. The combination of DoS and FDI attacks on the actuator signal can have a severe impact, endangering the secure functioning of DC microgrid and potentially destabilizing the entire system. To address these concerns, this chapter introduces an attack detection and mitigation method for mixed FDI and DoS attacks. This detection mechanism uses a state-machine-based technique with a dynamic signature function. The dynamic function constantly compares the actual signal of the actuator with its estimated signal. A Linear Unknown Input Functional Observer (LUIFO) can mitigate mixed attacks and provide an estimated actuator signal to the signature function. This detection and mitigation method ascertain the security and stable functioning of the DC microgrid system. The MATLAB simulation results on four distributed generators-based DC microgrid demonstrate the performance of the methods.

For the implementation of distributed cooperative control of the dc microgrid (DCMG), dependency on the communication layer is inevitable. Consequently, the DCMG, a typical cyber-physical system, is susceptible to cyber-attacks, which can hinder the achievement of overall control objectives, and the system may even get destabilized. To detect and mitigate cyber-attacks on actuators as well as sensors, a resilient mechanism is proposed in this article. First, the detection mechanism is proposed using a finite-state machine model-based technique that exploits a dynamic function. This function keeps investigating the actual and estimated values of actuator and sensor signals to authenticate the attack detection. Second, mitigation of attacks is carried out using a robust sliding-mode functional observer (SMFO). The signal of the compromised node is replaced by the SMFO estimated value in the event of an attack detection. The SMFO-based proposed technique is insensitive to measurement noise and parametric variations. The proposed approach is validated by simulation as well as experimental studies on a prototype of a 4-node DCMG.