A Comparative Study of Decision Tree, RNNs and CNNs for Detection of False Data Injection Attacks in Cyber-Physical Power Systems

Abstract

External connectivity for smart grid involving internet, data equipment, relays and breakers is essential to provide reliable and secure power supply. However, their interconnectivity also makes the grid susceptible to external threats with potential to damage equipment and cause power supply disruptions and safety hazards like false data injection attacks (FDIAs). In this paper, intrusion detection for FDIAs is proposed using three approaches. (1) A stack-based model containing an expansion decision tree and neural network, (2) recurrent neural metworks (RNNs) and (3) convolutional neural networks (CNNs). The experiment evaluation is performed using a publicly available dataset of Mississippi State University and Oak Ridge Nation Laboratory. On the provided dataset, the top performance is achieved on image-based classification using ResNet-18 with accuracy, precision, recall and F1 score of 97%, 97%, 95% and 96%, respectively. The DNN-GRU framework achieved accuracy, precision, recall and F1 score of 88%, 78%, 72% and 75%, respectively. Similarly, a version of the stack model of expansion decision tree and neural network combination achieved accuracy, precision, recall and F1 score of 95%, 95%, 95% and 95%, respectively. Each of these proposed methods has different preprocessing steps with different results. ResNet 18 has outperformed the hybrid model and recurrent neural network in precision, recall, F1 score and accuracy, which results in correct predictions, better identifying true positives (recall), avoiding false positives (precision) and achieving a robust balance between them (F1 score).