A Location Spoofing Detection Method for Social Networks (Short Paper)

It is well known that check-in data from location-based social networks (LBSN) can be used to predict human movement. However, there are large discrepancies between check-in data and actual user mobility, because users can easily spoof their location in LBSN. The act of location spoofing refers to intentionally making false location, leading to a negative impact both on the credibility of location-based social networks and the reliability of spatial-temporal data. In this paper, a location spoofing detection method in social networks is proposed. First, Latent Dirichlet Allocation (LDA) model is used to learn the topics of users by mining user-generated microblog information, based on this a similarity matrix associated with the venue is calculated. And the venue visiting probability is computed based on user historical check-in data by using Bayes model. Then, the similarity value and visiting probability is combined to quantize the probability of location spoofing. Experiments on a large scale and real-world LBSN dataset collected from Weibo show that the proposed approach can effectively detect certain types of location spoofing.

[1]  Sead Muftic,et al.  Location-Based Authentication and Authorization Using Smart Phones , 2012, 2012 IEEE 11th International Conference on Trust, Security and Privacy in Computing and Communications.

[2]  John Zimmerman,et al.  I'm the mayor of my house: examining why people use foursquare - a social-driven location sharing application , 2011, CHI.

[3]  Jianhua Lin,et al.  Divergence measures based on the Shannon entropy , 1991, IEEE Trans. Inf. Theory.

[4]  Gang Wang,et al.  On the validity of geosocial mobility traces , 2013, HotNets.

[5]  Daniel Z. Sui,et al.  True lies in geospatial big data: detecting location spoofing in social media , 2016, Ann. GIS.

[6]  Evangelos P. Markatos,et al.  The man who was there: validating check-ins in location-based services , 2013, ACSAC.

[7]  Omer Levy,et al.  word2vec Explained: deriving Mikolov et al.'s negative-sampling word-embedding method , 2014, ArXiv.

[8]  Michael I. Jordan,et al.  Latent Dirichlet Allocation , 2001, J. Mach. Learn. Res..

[9]  Gang Wang,et al.  "Will Check-in for Badges": Understanding Bias and Misbehavior on Location-Based Social Networks , 2021, ICWSM.

[10]  Mark Steyvers,et al.  Finding scientific topics , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[11]  Xue Liu,et al.  Location Cheating: A Security Challenge to Location-Based Social Network Services , 2011, 2011 31st International Conference on Distributed Computing Systems.

[12]  Christos Faloutsos,et al.  Spotting misbehaviors in location-based social networks using tensors , 2014, WWW.

[13]  Sameer Patil,et al.  "Check out where I am!": location-sharing motivations, preferences, and practices , 2012, CHI Extended Abstracts.