Ecsy-Recsy: Considering Sybil attack with time dynamics and economics in recommender system

There is no doubt on the popularity of online social networks (OSNs) nowadays. With the exponential growth of the OSNs, various recommender systems (RSs) are widely deployed onto the OSNs. The RSs provide users with customized information to the RSs´ users. Meanwhile, nefarious users attempt to compromise the RSs at the same time. There have been a lot of researches to block or detect such the nefarious users in the last decade. However, almost all studies aim to prevent such an anomaly under assumptions that the attackers have unlimited resources with static time condition. We consider the attackers´ economics and time dynamics by introducing the concept of stickiness and persistence in the RSs. Furthermore, we suggest an anomaly detection scheme named Ecsy-Recsy (Economics-based sybil detection in Recommender system) leveraging the stickiness and persistence. We evaluate the stickiness and persistence from experiment with our dataset in which we crawled from a real world RS. Our experiment shows that the stickiness and persistence are the powerful measures to reveal the condition of RS in dynamically changing time domain. The Ecsy-Recsy shows the good detection performance under the various anomalies (naive, random, and average Sybil attack).