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.
KeywordsLocation spoofing Social networks Semantic analysis
This work is supported by the cyberspace security Major Program in National Key Research and Development Plan of China under grant 2016YFB0800201, Natural Science Foundation of China under grants 61572165 and 61702150, State Key Program of Zhejiang Province Natural Science Foundation of China under grant LZ15F020003, Key Research and Development Plan Project of Zhejiang Province under grants 2017C01062 and 2017C01065, and the Scientific Research fund of Zhejiang Provincial Education Department under grant Y201737924, and Zhejiang Provincial Natural Science Foundation of China under Grant No. LGG18F020015.
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