Un Système de Détection D’Intrusion pour les Smart Grids

Abstract

Traditional power grids, with one-way communication, lack flexibility and efficient fault management. On the other hand, Smart Grids integrate advanced technologies, allowing bidirectional energy management and better adaptation to needs, while optimizing the integration of renewable energies. However, their interconnection makes them vulnerable to cyberattacks, which can lead to outages and risks for critical infrastructures. Securing these networks is therefore essential. This thesis proposes an intrusion detection system based on machine learning, combining CNN and LSTM, to improve the security of Smart Grids. Tested on the KDD99 dataset, the model showed good performance in terms of accuracy, outperforming existing methods.