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首页> 外文期刊>Journal of Water Resources Planning and Management >Real-Time Identification of Cyber-Physical Attacks on Water Distribution Systems via Machine Learning-Based Anomaly Detection Techniques
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Real-Time Identification of Cyber-Physical Attacks on Water Distribution Systems via Machine Learning-Based Anomaly Detection Techniques

机译：通过基于机器学习的异常检测技术实时识别供水系统的网络物理攻击

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Smart water infrastructures are prone to cyber-physical attacks that can disrupt their operations or damage their assets. An algorithm was developed to identify suspicious behaviors in the different cyber-physical components of a smart water distribution system. The algorithm incorporated multiple modules of anomaly-detection techniques to recognize different types of anomalies in the real-time monitoring and control data. Trained artificial neural networks were used to detect unusual patterns that do not conform to normal operational behavior. Principal component analysis was conducted to decompose the high-dimensional space occupied by the sensory data to uncover global anomalies. The algorithm was trained using a historical data set of trusted observations and tested against a validation and a test data set, both featuring a group of simulated attack scenarios. The proposed approach successfully identified all the attacks featured in the Battle of the Attack Detection Algorithms (BATADAL) data sets with high sensitivity and specificity. Nevertheless, the performance was sensitive to high background noise in the sensory data. (C) 2018 American Society of Civil Engineers.
机译：智能水基础设施易于遭受网络物理攻击，这可能会破坏其运营或破坏其资产。开发了一种算法来识别智能水分配系统的不同网络物理组件中的可疑行为。该算法结合了异常检测技术的多个模块，以识别实时监视和控制数据中的不同类型的异常。受过训练的人工神经网络用于检测不符合正常操作行为的异常模式。进行主成分分析以分解感官数据所占据的高维空间，以发现全局异常。该算法使用了受信任观察的历史数据集进行了训练，并针对验证和测试数据集进行了测试，两者均具有一组模拟攻击场景。所提出的方法成功地以高灵敏度和特异性识别了攻击检测算法之战（BATADAL）数据集中的所有攻击。然而，该性能对感官数据中的高背景噪声敏感。 （C）2018美国土木工程师学会。

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