A device fingerprint or machine fingerprint or browser fingerprint is information collected about a remote computing device for the purpose of identification. Fingerprints can be used to fully or partially identify individual users or devices even when cookies are turned off.
Basic web browser configuration information has long been collected by web analytics services in an effort to accurately measure real human web traffic and discount various forms of click fraud. With the assistance of client-side scripting languages, collection of much more esoteric parameters is possible. Assimilation of such information into a single string comprises a device fingerprint. In 2010, EFF measured at least 18.1 bits of entropy possible from browser fingerprinting, but that was before the advancements of canvas fingerprinting, which claims to add another 5.7 bits.
Recently such fingerprints have proven useful in the detection and prevention of online identity theft and credit card fraud. In fact, device fingerprints can be used to predict the likelihood users will commit fraud based on their signal profile, before they have even committed fraud.
Prior to early 2017, device fingerprinting was limited to single browsers. If a user switched browsers regularly, fingerprinting could not be used to link the user to these browsers. A cross browser fingerprinting method has been published which allows tracking of a user across multiple browsers on the same device.
Video Device fingerprint
Essentials
Motivation for the device fingerprint concept stems from the forensic value of human fingerprints. In the "ideal" case, all web client machines would have a different fingerprint value (diversity), and that value would never change (stability). Under those assumptions, it would be possible to uniquely distinguish between all machines on a network, without the explicit consent of the users themselves.
In practice neither diversity nor stability is fully attainable, and improving one has a tendency to adversely impact the other.
- Diversity requires that no two machines have the same fingerprint. However, large numbers of machines are likely to have exactly the same configuration data and thus the same fingerprint. This is particularly true in the case of factory installed operating systems. One remedy is to use a scripting language to harvest a large numbers of parameters from the client machine; however, this is likely to reduce stability, as there are more parameters that may change over time.
- Stability requires that fingerprints remain the same over time. However, by definition browser configuration preferences are not tamper proof. For example, if one measured attribute is whether the browser has cookies on or off, then a simple change of that setting is sufficient to change the fingerprint. One remedy is to reduce the number of parameters collected to only those that are very unlikely to change; however, this is likely to reduce diversity, as fewer parameters are being measured.
Maps Device fingerprint
Active vs passive collection
Fingerprinting methods range from passive to active.
Passive fingerprinting refers to techniques which do not involve the obvious querying of the client machine. These methods rely upon precise classification of such factors as the client's TCP/IP configuration, OS fingerprint, IEEE 802.11 (wireless) settings, and hardware clock skew.
Active fingerprinting assumes the client will tolerate some degree of invasive querying. The most active method is installation of executable code directly on the client machine. Such code may have access to attributes not typically available by other means, such as the MAC address, or other unique serial numbers assigned to the machine hardware. Such data is useful for fingerprinting by programs that employ digital rights management.
OSI model fingerprints
Passive collection of device attributes below the web-browser layer may occur at several OSI model layers. In normal operation, various network protocols transmit or broadcast packets or headers from which one may infer client configuration parameters. Sorted by layer, some examples of such protocols are:
- OSI Layer 7: SMB, FTP, HTTP, Telnet, TLS/SSL, DHCP
- OSI Layer 5: SNMP, NetBIOS
- OSI Layer 4: TCP (see TCP/IP stack fingerprinting)
- OSI Layer 3: IPv4, IPv6, ICMP, IEEE 802.11
- OSI Layer 2: CDP
Limitations
Collection of device fingerprints from web clients (browser software) relies on the availability of JavaScript or similar client-side scripting language for the harvesting of a suitably large number of parameters. Two classes of users with limited client-side scripting are those with mobile devices and those running privacy software or browser extensions which block ads and trackers.
A separate issue is that a single device may have multiple web clients installed, or even multiple virtual operating systems. As each distinct client and OS has distinct internal parameters, one may change the device fingerprint by simply running a different browser on the same machine, unless a new cross browser fingerprinting technique is used.
Criticisms
Consumers and their advocacy groups may consider covert tracking of users to be a violation of user privacy. Computer security experts may consider the ease of bulk parameter extraction to be a browser security hole.
See also
- Canvas fingerprinting
- Evercookie
- Browser security
- Browser sniffing
References
External links
- Julia Angwin, Jennifer Valentino-DeVries: Race Is On to 'Fingerprint' Phones, PCs The Wall Street Journal, 29 November 2010
Source of the article : Wikipedia
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