An Efficient Geolocation Method for Malicious LBSD Users Based on Dynamic Adjustment of Probes

Abstract

The integration of the Internet of Things (IoT) and social networks is a promising trend of network technology. However, the diversity of social networks also poses potential risks to IoT security. Researching on the geolocation of social network users can verify the effectiveness of location protection mechanisms adopted by service providers, as well as provide a means for geolocating miscreants in social networks. Most current research focuses on how to infer the true location of a target within a specific region, such as within a city, while less research has been done on how to achieve fast and accurate localization of targets under long-range conditions. In this manuscript, an efficient localization method for LBSD users at long distances based on dynamic adjustment of probes (DAPL) is proposed. Based on the analysis of factors that affect the accuracy and efficiency of the target location approximation, DAPL can approach the real location sustainability of the target by dynamically generating probe locations. By identifying abnormal fluctuations of the target’s reported distance, timely corrections of probe location are made to improve efficiency. In experimental results for Momo, a global popular LBSD social platform with more than 115 million active users show that even the initial probe is thousands of miles away from the target, DAPL can geolocate the target with a success rate close to 100% (99.5%), which is much higher than 70.6% of the existing method. Only about 12 times of LBSD service queries are needed, and DAPL can geolocate 88.9% of targets within 40 meters with an average error of 22.1 meters, which has higher efficiency and approximate accuracy compared with the existing typical method.