Authors : Sony Venugopal; Yogini S.; Sameeksha M. V.

Volume/Issue : Volume 11 - 2026, Issue 6 - June

Google Scholar : https://tinyurl.com/bdfmz3dc

Scribd : https://tinyurl.com/hwp6py3t

DOI : https://doi.org/10.38124/ijisrt/26jun622

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : False Data Injection (FDI) attacks represent a critical cyber-security threat to modern smart grids, as they deliberately manipulate measurement data used for system monitoring and control while remaining difficult to detect using conventional techniques. This paper presents a machine learning-based framework for the detection and localization of FDI attacks in power distribution systems. An IEEE 5-Bus test system is modeled and simulated in the MATLAB/Simulink environment to generate voltage measurements under normal operating conditions. FDI attack scenarios are created by selectively altering bus voltage data to emulate compromised measurement states without disturbing the physical dynamics of the system. Voltage magnitude features extracted from the simulation data are used to train a Random Forest classifier for identifying abnormal operating conditions and localizing the attacked bus. Simulation results demonstrate that the proposed approach effectively detectsThe results highlight the potential of integrating power system simulation with datadriven machine learning techniques to enhance cyber-security and situational awareness in smart grid applications.

Keywords : False Data Injection Attack, Smart Grid Cyber Security, IEEE 5-Bus System, Machine Learning, Random Forest, Attack Detection and Localization.

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