Tracking your browser with high-performance browser fingerprint recognition model

Abstract

As the cyber security has attracted great attention in recent years, and with all kinds of tools' (such as Network Agent, VPN and so on) help, traditional methods of tracking users like log analysis and cookie have been not that effective. Especially for some privacy sensitive users who changed their browser configuration frequently to hide themselves. The Browser Fingerprinting technology proposed by Electronic Frontier Foundation (EFF) gives a new approach of tracking users, and then our team designed an enhanced fingerprint dealing solution based on browser fingerprinting technology. Our enhanced solution plays well in recognizing the similar fingerprints, but it is not that efficient. Nowadays we improve the algorithm and propose a high-performance, efficient Browser Fingerprint Recognition Model. Our new model reforms the fingerprint items set by EFF and propose a Fingerprint Tracking Algorithm (FTA) to deal with collected data. It can associate users with some browser configuration changes in different periods of time quickly and precisely. Through testing with the experimental website built on the public network, we prove the high-performance and efficiency of our algorithm with a 20% time-consuming decrease than ever.