Automatic Data Extraction from Online Social Network and Track the Changes in Vulnerability

Abstract

The popularity of Online Social Network has increased huge amount of personal data to be found on Online Social Network and those data may get vulnerable. This makes people vulnerable to social engineering attacks because their personal data are readily available. In this research work, an automated data extraction tool is developed and stored them in repository. An online social network graph was generated according to the data stored in repository. Here node represents people’s profile. The graph analysis identifies structural features of the node such as indegree, outdegree, degree centrality, closeness centrality, betweenness centrality, and clustering coefficient. These all graph features are calculated, using social network analysis tool i.e. Node XL, according to likes and comments of the status posted by a user. The node is said to be vulnerable node, if the value of clustering coefficient is towards 1. It depends on the high number of interaction of those friends who have more mutual friends. The vulnerability of a node may change during the change of time. Timestamp is given for those nodes whose vulnerability changes during the change of time was evaluated again by analyzing the previous status with new updates.