Composite Modeling for Identity Security in Social Networks

Abstract

In this work, we aim at building a bridge from coarse behavioral data to an effective,
quick-response, and robust behavioral model for online identity theft detection. We
concentrate on this issue in online social networks (OSNs) where users usually have
composite behavioral records, consisting of multidimensional low-quality data, e.g.,
offline check-ins and online user-generated content (UGC). As an insightful result, we
validate that there is a complementary effect among different dimensions of records
for modeling users’ behavioral patterns. To deeply exploit such a complementary
effect, we propose a joint (instead of fused) model to capture both online and offline
features of a user’s composite behavior. We evaluate the proposed joint model by
comparing it with typical models and their fused model on two real-world datasets:
Foursquare and Yelp. The experimental results show that our model outperforms the
existing ones, with the area under the receiver operating characteristic curve (AUC)
values 0.956 in Foursquare and 0.947 in Yelp, respectively. Particularly, the recall
(true positive rate) can reach up to 65.3% in Foursquare and 72.2% in Yelp with the
corresponding disturbance rate (false-positive rate) below 1%. It is worth mentioning
that these performances can be achieved by examining only one composite behavior,
which guarantees the low response latency of our method. This study would give the cyber security community new insights into whether and how real-time online identity
authentication can be improved via modeling users’ composite behavioral patterns