  Cyberattack susceptibilities are introduced as the communication requirement
increases with the incorporation of more renewable energy sources into DC
microgrids. Parallel DC-DC converters are utilized to provide high current and
supply the load. Nevertheless, these systems are susceptible to cyberattacks
that have the potential to disrupt operations and jeopardize stability. Voltage
instability may result from the manipulation of communication commands and
low-layer control signals. Therefore, in this paper, a cyberattack that
specifically targets parallel DC-DC converters is examined in a DC microgrid. A
hybrid machine learning-based detection and mitigation strategy is suggested as
a means to counteract this threat. The false data injection (FDI) attack
targeting the converters is investigated within a DC microgrid. The efficacy of
the suggested approach is verified via simulations executed for various
scenarios within the MATLAB/Simulink environment. The technique successfully
identifies and blocks FDI attacks, preventing cyberattacks and ensuring the
safe operation of the DC microgrid.
