Identification of Denial of Services Attack in Smart Grids Using Machine Learning Algorithms

Smart grids are essentially power systems integrated with digital technology in energy networks for real-time monitoring and management of energy distribution. They operate based on wireless sensor networks, collecting data and enabling data communication. Smart grids can automatically isolate and fix problems, such as faults or cyberattacks. Combining machine learning (LSTM and Random Forest) for real-time anomaly detection with the Ethereum blockchain to ensure secure, tamper-proof anomaly logging. Combining machine learning (LSTM and Random Forest) for real-time anomaly detection with the Ethereum blockchain to ensure secure, tamper-proof anomaly logging. The ML models can identify irregular patterns in the time-series data of WSNs, while blockchain provides decentralized and transparent storage. The model achieved anomaly detection with negligible latency while ensuring secure communication. The immutability of blockchain adds to trust and makes it feasible for large critical infrastructures such as smart grids.