Exploratory Data Analysis and Recurrent Expansion for Power Systems Cybersecurity Forensics

The increasing reliance on advanced power systems in critical infrastructure highlights the need to enhance cybersecurity resilience. This study tackles this crucial issue through Exploratory Data Analysis (EDA) and Representation Learning (RL). Several real-world, complex power system datasets characterized by their large size, dynamic nature, and high imbalance are examined. EDA serves as a fundamental step to provide insights into power system event scenarios, supported by a comprehensive preprocessing strategy that includes managing missing values, reducing dimensionality, denoising, removing outliers, and addressing class imbalance. Missing values are thoughtfully handled by evaluating mean values. Dimensionality reduction involves removing insignificant features that might provide misleading information, along with integrating Principal Component Analysis (PCA). Class-specific denoising and outlier removal follow, with denoising utilizing various algorithms and a diverse range of wavelet functions to improve data quality. Robust outlier removal is achieved through iterative analyses and distance-based techniques. To address class imbalance, the Synthetic Minority Over-Sampling Technique (SMOTE) is employed, ensuring a balanced and representative dataset. For RL, deep learning models such as Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), Gated Recurrent Units (GRU), and their Multiverse Recurrent Expansions with Multiple Repeats (MV-REMR) are used in a comparative study. The results demonstrate promising and satisfactory outcomes for all models, with MV-REMR delivering the best performance. This study integrates diverse techniques into a unified EDA and RL approach, offering valuable insights into power system cybersecurity forensics, addressing current challenges, and promoting resilient and adaptive cybersecurity measures in power systems.