Deep Learning Strategies for Detecting and Mitigating Cyber-Attacks Targeting Water-Energy Nexus

Due to the substantial interdependencies between water and energy infrastructures, the water-energy nexus (WEN) is regarded as an appealing focal point for potential malicious intruders, particularly in the context of smart cities. Driven by the increasing vulnerability of these critical infrastructures to sophisticated cyber-attacks and the lack of comprehensive protection strategies, this study aims to address these challenges. This paper introduces a pioneering deep learning-driven framework for detecting, locating, and mitigating stealthy false data injection attacks (FDIAs) targeting electrical and water networks within the WEN. The proposed framework, which leverages two lightweight deep learning models, is designed to accurately detect and locate cyber-attacks in real-time while effectively mitigating their impact, thereby enhancing the operational resilience and security of the WEN. These models undergo training and testing using a wide range of challenging cyber-attack scenarios, including Pulse, Random, Ramp, and Scaling attacks, to ascertain their efficacy and resilience. The detection model accurately identifies and locates the cyber-attacks, achieving a remarkable accuracy rate of 99.73% for identifying attacks aimed at the electrical network and 96.66% for detecting attacks directed at the water network. After detecting the cyber-attack, the mitigation model reconstructs manipulated measurements to ensure the continued operation of the WEN and prevent the occurrence of disruptions or failures. Remarkably, the model attains an outstanding recovery success rate of 98.98% for electrical measurements and 87.64% for water measurements. Additionally, it effectively reduces the operational cost increase (OCI) due to cyber-attacks from 2.19% to 0.0048% for the electrical network and from 5.17% to 0.34% for the water network. The dataset employed in this study is characterized by its imbalanced nature, with cyber-attack instances being infrequent compared to normal instances. Consequently, the adoption of balanced affinity loss, designed to tackle the dataset's inherent imbalance, achieves superior performance, with an accuracy of 99.73% and an area under the curve (AUC) of 99.86%. The proposed framework offers a robust and efficient solution for safeguarding the WEN against cyber-attacks, making it a valuable tool for enhancing the resilience and security of smart city infrastructures.