A Hybrid CNN-LSTM Deep Learning Model for Intrusion Detection in Smart Grid
2025-09-10
Summary
The article discusses a hybrid deep learning model combining Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks to enhance intrusion detection in smart grids. By leveraging CNN for spatial feature extraction and LSTM for temporal pattern recognition, the model achieved high accuracy (99.70%) in detecting cyber threats using DNP3 and IEC104 datasets, significantly outperforming other deep learning approaches.
Why This Matters
As smart grids become more integrated with digital technologies, they face increased vulnerabilities to cyberattacks, which can disrupt operations and cause significant damage. The research highlights the importance of robust intrusion detection systems to protect these critical infrastructures. The proposed CNN-LSTM model offers a promising solution by enhancing threat detection accuracy and ensuring smart grid security.
How You Can Use This Info
Professionals working with smart grid technologies can leverage this hybrid CNN-LSTM model to improve the cybersecurity measures of their systems. By adopting advanced deep learning techniques, organizations can better detect and mitigate potential cyber threats, ensuring the reliability and stability of their energy distribution networks. This approach can also guide the development of future cybersecurity strategies in other critical infrastructure sectors.