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In this paper, the authors propose a data-driven approach for security management in a network that interacts or receives inputs from physical systems - including human behavior. Their goal is to leverage the unique features of cyberphysical systems. In particular they propose: (1) the use of historical data from physical systems and human behaviors to enable anomaly detection, (2) the use of contextual data from multiple and diverse sensor readings to obtain a higher-level collective vision of the network for better event correlation and decision analysis, and (3) the use of physical sensor data and human behavior to enable fine-grained, dynamic access control and implicit authentication. They outline use cases describing how their ideas can be applied in the Home Area Network (HAN).
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