Matching social network biometrics using geo-analytical behavioral modeling

Abstract

Social patterns and graphical representation of geospatial activity is important for describing a person's typical behavior. We discuss a framework using social media and GPS smart phone to track an individual and establish normal activity with a network biometric. An individual's daily routine may include visiting many locations - home, work, shopping, entertainment and other destinations. All of these activities pose a routine or status quo of expected behavior. What has always been difficult, however, is predicting a change to the status quo, or predicting unusual behavior. We propose taking the knowledge of location information over a relatively long period of time and marrying that with modern analytical capabilities. The result is a biometric that can be fused and correlated with another's behavioral biometric to determine relationships. Our solution is based on the analytical environment to support the ingestion of many data sources and the integration of analytical algorithms such as feature extraction, crowd source analysis, open source data mining, trends, pattern analysis and linear game theory optimization. Our framework consists of a hierarchy of data, space, time, and knowledge entities. We exploit such statistics to predict behavior or activity based on past observations. We use multivariate mutual information as a measure to compare behavioral biometrics.