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ADCMO: An Anomaly Detection Approach Based on Local Outlier Factor for Continuously Monitored Object

Since the existing works in data stream anomaly detection mainly locate abnormal objects from data sequences instead of detecting the abnormal state of a monitored object in real-time. And they need a set of empirical predefined thresholds, which may affect the flexibility of the detection models. These limitations make the existing solutions no longer suitable for the real-time detection of the changes in the status of the monitored object. In this paper, we propose a new Abnormal conditions Detection approach (ADCMO) on the Continuously Monitored Object. ADCMO incorporates the data distribution property to carry out the anomaly quantification for the data in the data stream, and then the abnormal conditions of the monitored object are quantified by the anomaly information of the current data and historical data. Finally, the anomaly state is adaptively determined by the distribution of anomaly coefficients. ADCMO can detect the changes in the status by calculating the outlier coefficient of the object at each moment and adaptively make the abnormal early warning. The experiments show that ADCMO can do this any-time, dynamically, efficiently and effectively.