A HYBRID CNN-LSTM DEEP LEARNING MODEL FOR INTRUSION DETECTION IN SMART GRID

The evolution of the traditional power grid into the "smart grid" has resulted in a fundamental shift in energy management which allows the integration of renewable energy sources with modern communication technology. However, this interconnection has increased smart grids' vulnerability to attackers, which might result in privacy breaches, operational interruptions, and massive outages. The SCADA-based smart grid protocols are critical for realtime data collecting and control, but they are vulnerable to attacks like unauthorized access and denial of service (DoS). This research proposes a hybrid deep learning-based Intrusion Detection System (IDS) intended to improve the cybersecurity of smart grids. The suggested model takes advantage of Convolutional Neural Networks' (CNN) feature extraction capabilities as well as Long Short-Term Memory (LSTM) networks' temporal pattern recognition skills. DNP3 and IEC104 intrusion detection datasets are employed to train and test our CNNLSTM model to recognize and classify the potential cyberthreats. Comparing to other deep learning approaches, the results demonstrate considerable improvements in accuracy, precision, recall, and F1- score, with a detection accuracy of 99.70%.