AHBSMO-DRN: Single Device and Multiple Sharing-Based Geo-Position Spoofing Detection in Instant Messaging Platform

Abstract

In recent years, location check-in on mobile components is a trending topic over social media. At the same time, hackers grasp the geographical position (geo-position) data that destruct the security of users. Hence, it is crucial to detect the originality of geo-position. A plethora of methods have been developed for geo-position spoofing identification that depends on geo-position data. Nonetheless, such techniques are incapable in terms of missing prior data or insufficient of large samples. To counterpart this issue, an effective model is invented to detect spoofing activity by Adaptive Honey Badger Spider Monkey Optimization_Deep residual Network (AHBSMO-based DRN). Here, neuro camera footprint refining is performed using Neuro Fuzzy filter and extracted footprint image obtained while considering the input and spoofed image are fused using Pearson correlation coefficient. Meanwhile, geo-tagged value of input image and spoofed image is also fused based on same Pearson coefficient. Finally, fusion is performed and then, spoofing detection is accomplished by comparing the Discrete Cosine Transform (DCT) foot print of two images to find if the input image is spoofed or not. Moreover, AHBSMO-based DRN model has gained outstanding outcomes in regard of accuracy of 0.921, True Positive Rate (TPR) 0 of 0.911, and False Positive Rate (FPR) of 0.136.