Browser Fingerprint Coding Methods Increasing the Effectiveness of User Identification in the Web Traffic

Abstract

Web-based browser fingerprint (or device fingerprint) is a tool used to identify and track user activity in web traffic. It is also used to identify computers that are abusing online advertising and also to prevent credit card fraud. A device fingerprint is created by extracting multiple parameter values from a browser API (e.g. operating system type or browser version). The acquired parameter values are then used to create a hash using the hash function. The disadvantage of using this method is too high susceptibility to small, normally occurring changes (e.g. when changing the browser version number or screen resolution). Minor changes in the input values generate a completely different fingerprint hash, making it impossible to find similar ones in the database. On the other hand, omitting these unstable values when creating a hash, significantly limits the ability of the fingerprint to distinguish between devices. This weak point is commonly exploited by fraudsters who knowingly evade this form of protection by deliberately changing the value of device parameters. The paper presents methods that significantly limit this type of activity. New algorithms for coding and comparing fingerprints are presented, in which the values of parameters with low stability and low entropy are especially taken into account. The fingerprint generation methods are based on popular Minhash, the LSH, and autoencoder methods. The effectiveness of coding and comparing each of the presented methods was also examined in comparison with the currently used hash generation method. Authentic data of the devices and browsers of users visiting 186 different websites were collected for the research.

Detecting Anomalies in Advertising Web Traffic with the Use of the Variational Autoencoder

“…the number of fields to be filled in without pressing a key, the number of text changes made, the number of the "Tab" keys is pressed, the number of mouse pointer clicks on controls, the number of data copied from the clipboard, the number of times individual mouse keys are pressed, the number of keys pressed on the keyboard, and information about mouse pointer movements ("counter", "has ws mouse", "has ws scroll", "mm unique points"). The parameters are described in more detail in papers [9] and [20].…”

The Gaze and Mouse Signal as additional Source for User Fingerprints in Browser Applications

“…Cross-browser fingerprinting was covered in [6] using both operating system and hardware features. A further extension of these approaches is the use of hashing algorithms to make the calculation and identification more effective [51]. Applications for browser-based fingerprints are described in the literature as user tracking [10,11], abuse prevention [75], and authentication [4] in many different contexts.…”