AI Driven Approach for Detecting False Data Injection Attacks Targeting Water-Energy Nexus

As water and energy grids are highly interconnected, water-energy nexus (WEN) is considered an attractive target for malicious attackers. Attacking WEN affects both systems simultaneously; consequently, the attack is able to create fatal damage in the entire network. In this paper, we propose to tackle this problem using different deep learning models with different loss functions. Additionally, a mixed-integer nonlinear programming (MINLP) WEN in presence of renewable energy sources model is reformulated into a mixed-integer linear programming model for real-time implementation purposes. The optimization model is developed using MATALB, and the deep-learning models are implemented using Python keras framework.