Detection of Sybil's across communities over Social Internet of Things

Abstract

Social Internet of Things is a new paradigm that integrates Internet of things and Social Networks. Several challenges exist in building Social Internet of Things (SIoT). Very limited research has been carried out in the past 7 years to build a reliable Social Internet of Things community. A major threat with Social Things is Sybil attacks. Since SloT is comprised of autonomous objects/nodes, tracking fake identities is an open problem. This paper proposes a new mechanism to identify Sybil's in communities of Social Internet of Things. This paper aims at (i) identifying communities among Social Internet of Things using Community-Infer algorithm. Using the properties of Social Networks and ACO heuristics various communities among the Social Internet of Things were identified, (ii) The communities are checked for existence of Sybil's. The algorithm Detect-Sybil detects and classifies the number of Sybil's in each communities. Compared to existing schemes the proposed method classifies communities accurately with a high modularity score.