Cyber-physical attacks on critical infrastructure, such as power systems, pose a significant threat to the stability and reliability of these systems. In this executive summary, we discuss the construction of three types of cyber-physical attacks (false data attack, replay attack, and hybrid attack) in the control loop of the excitation system and governor system on the IEEE 9 bus model. Particle filter algorithm is known to be effective in estimating system states in real-time and detecting anomalies in noisy environments, making them suitable for power system cybersecurity. Our research, therefore, focuses on implementing the Particle Filter Detection Algorithm within the Real-Time Digital Simulator (RTDS) to identify and thwart cyber-physical attacks. The RTDS provides a controlled and realistic environment for testing and validating the algorithm's performance. By doing so, we aim to fortify the defences of critical power infrastructure against potential threats. Additionally, we evaluate the performance of the Particle Filter Algorithm using a confusion matrix. This matrix allows us to quantitatively measure the algorithm's effectiveness in terms of true positive, true negative, false positive, and false negative. These metrics provide insights into the accuracy, precision, recall, and overall detection capability of the algorithm. The accuracy of the Particle Filter Algorithm is paramount in safeguarding critical infrastructure like power systems. High accuracy ensures that genuine attacks are promptly identified and thwarted, while minimizing false alarms that could disrupt normal system operations and erode confidence in the cybersecurity measures. In conclusion, the threat posed by cyber-physical attacks on critical infrastructure, such as power systems, is undeniable. The construction and evaluation of cyber-physical attacks on the IEEE 9 bus model and the implementation of the Particle Filter Detection Algorithm in the RTDS represent critical steps toward enhancing power system cybersecurity. The use of a confusion matrix provides a quantitative assessment of the algorithm's performance, shedding light on its strengths and areas for improvement. As we move forward in our quest to secure critical infrastructure, such research and methodologies will play an increasingly pivotal role in defending against the ever-evolving landscape of cyber threats.