Detection and Verification of Cloned Profiles in Online Social Networks Using MapReduce Based Clustering and Classification
Keywords:
Cloned profile detection, identity cloning attack, online social networks, parallel k-means, parallel SVM, mapreduceAbstract
The use of online social networks has become an unavoidable part of today's humans’ life. The augmented usage of these online social networks has also increased the misusage through spreading spam messages, false reviews and fake news. Fake accounts are one such method in which attackers clone the profiles of innocent victims to harm them and damage their reputations. In some situations, fake accounts are also created to steal money from other users. In recent days, the detection of such cloned or fake profiles has perceived a wide range of attention from researchers. However, the existing methods lack accuracy and precision in detecting cloned profiles. In this paper, an effort has been made to identify the cloned profiles using clustering and classification process. The method collects the fake and possible cloned profiles and computes the additional relationship attributes for performing cloned profile detection. The parallel k-means clustering is carried out to group the suspicious profiles that are similar to the real ones. Then the parallel SVM classification is applied to the cluster in which the real profile is grouped. Finally, the classification results are verified using the attribute and network similarity measure. The various stages of the proposed model are implemented using the MapReduce framework which is more suitable for big data. The experimental analysis and results indicate that the proposed model has better performance than other competitors with an accuracy and precision of 98.19% and 98.96% for the MIB twitter dataset and 98.90% and 99.17% for the synthetic dataset created for the study.
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