Facebook Traffic Pattern Analytics

This paper presents a study of applying a new data pattern recognition approach, called Product Coefficients (PCs), to discover patterns of Facebook network and service traffic. It includes three parts: 1) collection of service response time and packages as the initial data set, 2) preprocessing of the data sets and discovers of PCs values as patterns of the data set. 3) displaying of the patterns for simple outlier detection. Additional research is ongoing related to the post processing of PCs values for prediction, e.g. using machine learning to decide if outliers are indicators of a security attack. The key contribution of this work is the application of PCs method to some practical Facebook traffic data.