This research paper introduces a hybrid deep learning model designed to safeguard smart grid infrastructures from increasingly sophisticated cyberattacks. By merging the spatial feature extraction of Convolutional Neural Networks with the temporal pattern recognition of Long Short-Term Memory networks, the authors created a system capable of identifying complex threats in real time. The study specifically addresses vulnerabilities within SCADA protocols like DNP3 and IEC 104, which are often targets for denial-of-service and data manipulation attacks. Experimental results using authentic datasets demonstrate that this combined architecture achieves an exceptional 99.70% detection accuracy. Ultimately, the document emphasizes that transitioning to advanced intrusion detection systems is vital for maintaining the reliability and security of modern, interconnected power networks.
This episode includes AI-generated content.