Building Artificial Identities in Social Network Using Semantic Information

Abstract

As the popularity of social networking sites increase, so does their attractiveness for criminals. In this work, we show how an adversary can build artificial identities using semantic information in social network. Our method make the identities look more like real people, therefore can be used to support many kinds of attacks, such as ASE, profile cloning. A prototype of this method is implemented, includes following stages: Firstly, categories of virtual identity are predefined, and each category has multiple properties, such as geographical region, hobby, education, age, interested topic/keywords, etc. Secondly, based on category information, each identity will foster its own "life" semantically, such as edit profile and update status, find hot related news/topic from Google then post to wall, find related groups/networks then request to add in, and find/like/create/comment pages/posts, etc. Thirdly, artificial identity will evolve to multiple stages according to its status (for example, number of friends of real people), single identity with different evolutionary stages is linked together to a group that will help to ensure the number of attack edges.