Abstarct: Abstract The integration of power networks with Information and Communication Technology (ICT) has given rise to a new concept known as the smart grid. Despite its numerous advantages, the smart grid is highly susceptible to cyber-attacks due to its connection with telecommunication systems. Among various types of cyber-attacks, False Data Injection (FDI) attacks are particularly complex and pose a significant risk to the smart grid. In FDI attacks, it is assumed that the attacker has access to the network configuration and can mislead operators in the control center by injecting erroneous data into measurement devices. This misguidance may lead operators to make incorrect decisions, potentially causing damage to the smart grid. The objective of this thesis is to propose a method for modeling and detecting FDI cyber-attacks. To achieve this, a number of network buses are protected by Phasor Measurement Units (PMUs), with the assumption that the information from these buses is secure and not subject to attack, thus referring to them as "trust buses." Using historical data from the trust buses, a Markov chain is constructed for each bus. The properties of the Markov chain enable predictions about future states baxsed on the current state probabilities. Additionally, the state estimation is derived from the data transmitted by network measurement equipment within the SCADA system, and these results are compared against the Markov chain. If the estimated state falls outside the predicted probabilities of the Markov chain, this indicates a high likelihood of a cyber-attack. The proposed method has been simulated on the IEEE 118-bus standard system. The simulation results demonstrate the effectiveness of the proposed approach in detecting FDI cyber-attacks.