Anomaly detection in diurnal CPS monitoring data using a local density approach

Devices that monitor and measure various system parameters or physical phenomena form an integral part of cyber-physical systems. Such devices usually operate continuously and gather important data that is often critical for the operation of the underlying system. Thus, it becomes important to understand and detect abnormal or malicious device behavior, false injection of data by an adversary, or other security threats that may lead to incorrect measurement data. This paper addresses the problem of detection of anomalies in diurnal traffic volume data in an intelligent transportation system. The proposed approach leverages the statistical properties of the data to perform anomaly detection by calculating the `local density' of the data points. Anomalous behavior in the traffic volumes reported by road segments is calculated based on sparse local density of the data points. Our approach for detecting anomalies does not require any information about the outside factors which might have influenced the data. The proposed approach has been evaluated on attacks simulated on transportation data collected by the New York State Department of Transportation. The proposed approach also extends to other cyber-physical systems where the monitored data exhibits diurnal patterns.