Detecting False Data Injection Attacks Using Machine Learning-Based Approaches for Smart Grid Networks

Abstract

Current electricity sectors will be unable to keep up with commercial and residential customers’ increasing demand for data-enabled power systems. Therefore, next-generation power systems must be developed. It is possible for the smart grid, an advanced power system of the future, to make decisions, estimate loads, and execute other data-related jobs. Customers can adjust their needs in smart grid systems by monitoring bill information. Due to their reliance on data networks, smart grids are vulnerable to cyberattacks that could compromise billing data and cause power outages and other problems. A false data injection attack (FDIA) is a significant attack that targets the corruption of state estimation vectors. The primary goal of this paper is to show the impact of an FDIA attack on a power dataset and to use machine learning algorithms to detect the attack; to achieve this, the Python software is used. In the experiment, we used the power dataset from the IoT server of a 10 KV solar PV system (to mimic a smart grid system) in a controlled laboratory environment to test the effect of FDIA and detect this anomaly using a machine learning approach. Different machine learning models were used to detect the attack and find the most suitable approach to achieve this goal. This paper compares machine learning algorithms (such as random forest, isolation forest, logistic regression, decision tree, autoencoder, and feed-forward neural network) in terms of their effectiveness in detecting false data injection attacks (FDIAs). The highest F1 score of 0.99 was achieved by the decision tree algorithm, which was closely followed by the logistic regression method, which had an F1 score of 0.98. These algorithms also demonstrated high precision, recall, and model accuracy, demonstrating their efficacy in detecting FDIAs. The research presented in this paper indicates that combining logistic regression and decision tree in an ensemble leads to significant performance enhancements. The resulting model achieves an impressive accuracy of 0.99, a precision of 1, and an F1 score of 1.