You are where you have been: Sybil detection via geo-location analysis in OSNs

Online Social Networks (OSNs) are facing an increasing threat of sybil attacks. Sybil detection is regarded as one of major challenges for OSN security. The existing sybil detection proposals that leverage graph theory or exploit the unique clickstream patterns are either based on unrealistic assumptions or limited to the service providers. In this study, we introduce a novel sybil detection approach by exploiting the fundamental mobility patterns that separate real users from sybil ones. The proposed approach is motivated as follows. On the one hand, OSNs including Yelp and Dianping allow us to obtain the users´ mobility trajectories based on their online reviews and the locations of their visited shops/restaurants. On the other side, a real user´s mobility is generally predictable and confined to a limited neighborhood while the sybils´ mobility is forged based on the paid review missions. To exploit the mobility differences between the real and sybil users, we introduce an entropy based definition to capture users´ mobility patterns. Then we design a new sybil detection model by incorporating the newly defined location entropy based metrics into other traditional feature sets. The proposed sybil detection model can significantly improve the performance of sybil detections, which is well demonstrated by extensive evaluations based on the data set from Dianping.