Year: 2026
Type: article
Abstract: The transition to smart grids integrated with information and communication technology (ICTs) for the Special Protection Group (SPG) not only increases operational efficiency but also creates opportunities for advanced Cyber-Physical Attacks (CPAs). These threats are often stealthy, hard to detect with current security measures, and can quickly evade machine learning (ML) approaches. This paper presents a framework designed for real-time detection and mitigation of CPAs in modern grids. The paper uses a hybrid feature-engineering method that combines physical power information from phasor measurement units (PMUs) and Supervisory Control and Data Acquisition (SCADA) with cyber-side network logs. Using statistical and time-series analysis on this fused information, the authors detected subtle evidence of malicious activity. The key to the developed scheme is a stacking-based ensemble learning framework that integrates XGBoost, Support Vector Machine, and Convolutional Neural Network models to ensure high detection accuracy and reliability. The dataset was divided into 80% for training and 20% for testing; experimental evaluation showed a high detection rate of 96% and a low false positive rate of 1.5% for False Data Injection (FDI), Denial of Service (DoS), and Replay attacks. The framework also provides low detection latency and high throughput, supporting real-time operation. Low latency includes Data acquisition, Data cleaning, Normalization, Synchronization, and windowing. The scheme not only detects threats but enable a context-aware response module to provide real-time isolation of components or rerouting of power in response. This keeps the grid stable even during an attack. The paper presents an advanced AI-powered, real-time defense against increasingly sophisticated cyber-physical threats.
Source: UMYU Scientifica
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W7212307372