Abstract
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.
Abstract (translated)
数字孪生（DT）通过使用实时数据、分析和预测模型来优化运营，正在改善水分配系统。本文介绍了一个专为西班牙供水网络设计的数字孪生平台，并利用长短期记忆（LSTM）网络来预测用水量。然而，机器学习模型容易受到诸如快速梯度符号法（FGSM）和投影梯度下降（PGD）等对抗性攻击的影响。这些攻击操纵关键的模型参数，在不影响数据外观的情况下注入细微扰动以降低预测准确性。为了进一步利用这些漏洞，我们引入了一种基于学习自动机（LA）和随机学习自动机的方法，该方法能够动态调整扰动，使对抗性攻击更难被检测到。实验结果表明，这种方法显著影响了预测的可靠性，导致平均绝对百分比误差（MAPE）从26%上升至超过35%。此外，适应性的攻击策略放大了这种效果，突显了AI驱动的数字孪生中的网络安全风险。这些发现强调了建立强大的防御措施的迫切需要，包括对抗性训练、异常检测和安全的数据管道。
URL
https://arxiv.org/abs/2504.20295
