Many essential infrastructures are built on Cyber-Physical Systems (CPS), which are complex systems that combine physical operations and computational components. Sensors, actuators, and control mechanisms that are man- aged by embedded computational and communication resources enable these systems to communicate with the real world in a range of applications, including smart grids, healthcare monitoring, transportation networks, and industrial automation. Because of their intrinsic interconnectedness and complexity, CPS are extremely vulnerable to different types of cyberattacks, which can negatively affect their dependability, security, and performance. Such attacks are common, which emphasizes how urgently we need sophisticated security solutions that can protect these essential systems. This study looks into the impact of simultaneous cyberattacks on CPS, investigating attack techniques such as remote hijacking, MitM attack, and DDoS, along with any possible repercussions. We assess how well the Sequential Monte Carlo Probability Hypothesis Density (SMCPHD) filter of the Random Finite Set (RFS) theory framework detects and mitigates these risks. By analyzing the time complexity and memory utilization of a parallel batch computing strategy in comparison to conventional sequential alternatives. The outcomes demonstrate significant decreases in processing time while maintaining effective memory use, providing a strong remedy for improving CPS security against several simultaneous attacks. One of the models to considers the quantity based on demand and investment in this research, compare the other model focus on realistic connection between the quantity, demand and the price.