The water-energy nexus is a critical infrastructure that requires continuous monitoring and protection against cyber threats. With the increasing integration of smart technologies and digital systems in water and energy management, the vulnerability to cyber attacks has escalated. This study proposes an innovative approach to detect and mitigate cyber attacks targeting the water-energy nexus by leveraging deep learning strategies. The model utilizes a combination of Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks to analyze time-series data from sensors and control systems in the water and energy sectors. By processing network traffic and system logs, the deep learning model is capable of identifying anomalies that indicate potential cyber threats such as Distributed Denial of Service (DDoS) attacks, malware, or data breaches. Furthermore, the proposed system includes a mitigation module that can take automated actions, such as rerouting traffic, isolating compromised systems, or triggering alerts for human intervention. The system is trained on a diverse dataset, including both normal and attack scenarios, enabling it to generalize across various attack types and real-world conditions. Preliminary results show that the deep learning-based approach achieves a high detection accuracy, with a precision of 96% and recall of 94%, significantly outperforming traditional methods. This research demonstrates the potential of deep learning in securing critical infrastructures, offering a robust solution for protecting the water-energy nexus from evolving cyber threats.
Keyword : Water-Energy Nexus, Cyber Attacks, Deep Learning, Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM), Anomaly Detection, Cybersecurity,