Digital twins (DTs) are improving water distribution systems by using real-time data, analytics, and prediction models to optimize operations. This paper presents a DT platform designed for a Spanish water supply network, utilizing Long Short-Term Memory (LSTM) networks to predict water consumption. However, machine learning models are vulnerable to adversarial attacks, such as the Fast Gradient Sign Method (FGSM) and Projected Gradient Descent (PGD). These attacks manipulate critical model parameters, injecting subtle distortions that degrade forecasting accuracy. To further exploit these vulnerabilities, we introduce a Learning Automata (LA) and Random LA-based approach that dynamically adjusts perturbations, making adversarial attacks more difficult to detect. Experimental results show that this approach significantly impacts prediction reliability, causing the Mean Absolute Percentage Error (MAPE) to rise from 26% to over 35%. Moreover, adaptive attack strategies amplify this effect, highlighting cybersecurity risks in AI-driven DTs. These findings emphasize the urgent need for robust defenses, including adversarial training, anomaly detection, and secure data pipelines.

本研究针对数字双胞胎在水资源预测中的应用，揭示了其在面对对抗攻击时的脆弱性。提出了一种基于学习自动机和随机学习自动机的方法，动态调整扰动以加大对抗攻击的隐蔽性，实验结果显示，这一方法显著提高了预测误差，凸显了人工智能驱动的数字双胞胎在网络安全方面的风险与防御需求。

数字双胞胎的阴暗面：对人工智能驱动水资源预测的对抗攻击