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The pervasiveness of GPS-enabled mobile devices and the widespread use oflocation-based services have resulted in the generation of massive amounts ofgeo-tagged data. In recent times, the data analysis now has access to moresources, including reviews, news, and images, which also raises questions aboutthe reliability of Point-of-Interest (POI) data sources. While previousresearch attempted to detect fake POI data through various security mechanisms,the current work attempts to capture the fake POI data in a much simpler way.The proposed work is focused on supervised learning methods and theircapability to find hidden patterns in location-based data. The ground truthlabels are obtained through real-world data, and the fake data is generatedusing an API, so we get a dataset with both the real and fake labels on thelocation data. The objective is to predict the truth about a POI using theMulti-Layer Perceptron (MLP) method. In the proposed work, MLP based on dataclassification technique is used to classify location data accurately. Theproposed method is compared with traditional classification and robust andrecent deep neural methods. The results show that the proposed method is betterthan the baseline methods.

Detecting Fake Points of Interest from Location Data