Smart grid control communication infrastructure is a fundamental component of modern cyber-physical power systems, enabling real-time monitoring, control, and coordination between physical power networks and digital communication platforms. However, the increasing integration of communication technologies with power grid operations has also introduced new security vulnerabilities, particularly malicious data manipulation attacks that can disrupt control signals, mislead monitoring systems, and cause instability in power distribution. Rapid detection of such attacks is critical because delayed response may lead to large-scale power failures, equipment damage, or cascading system faults. Therefore, the development of an efficient and fast detection mechanism for identifying manipulated data within smart grid communication channels has become an important research challenge.

This research investigates a quick detection method for malicious data manipulation in smart grid control communication infrastructure using a cyber-physical system–based monitoring framework. The study proposes a detection model that analyzes communication behavior, control signal consistency, and network reliability indicators to identify abnormal data injection in real time. The proposed approach integrates communication monitoring, anomaly evaluation, and reliability analysis to recognize suspicious changes in control messages before they affect the physical power system. The framework is designed to operate in distributed smart grid environments where multiple nodes exchange control information through communication networks.

The methodology is based on the analysis of cyber-physical power system architecture, communication reliability modeling, and anomaly detection principles used in secure smart grid operation. The proposed detection mechanism evaluates communication patterns, synchronization behavior, and control signal integrity to identify manipulated data with minimal delay. Simulation results demonstrate that the proposed approach can effectively detect abnormal data modification while maintaining stable performance under varying network conditions. The detection model reduces false alarms and improves response speed compared with conventional monitoring methods.

The findings indicate that quick detection of malicious data manipulation significantly improves the security and reliability of smart grid communication infrastructure. The proposed framework can support real-time monitoring in modern cyber-physical power systems and may be extended to other critical infrastructures that rely on secure communication networks.