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In this paper, we propose and evaluate the application of unsupervised machine learning to anomaly detection for a Cyber-Physical System (CPS). We compare two methods: Deep Neural Networks (DNN) adapted to time series data generated by a CPS, and one-class Support Vector Machines (SVM). These methods are evaluated against data from the Secure Water Treatment (SWaT) testbed, a scaled-down but fully...
GPS-enabled devices have greatly facilitated the continuous collection of highly accurate locational data for moving objects including humans. The effective and efficient detection of mobility mode from raw GPS data is critically important for social behavior and public health research. In this paper, we propose a new method that can be used to automate the detection of human mobility mode by synthetically...
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