Device fingerprint


A device fingerprint (or machine fingerprint) is a compact summary of software and hardware settings collected from a remote computing device.

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[1][2]. Assimilation of such information into a single string comprises a device fingerprint.

Recently such fingerprints have proven useful in the detection and prevention of online identity theft and credit card fraud[3][4].

Contents

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, even 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.

Active vs Passive Collection

Fingerprinting methods range from passive to active.

Passive fingerprinting occurs without 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[5], and hardware clock skew[6].

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. A drawback is that installed software is an easy target for tampering.

OSI Layer 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:

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.

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.

Criticisms

Consumers and their advocacy groups may consider covert tracking of users to be a violation of user privacy[10]. Computer security experts may consider the ease of bulk parameter extraction to be a browser security hole[11].

References

  1. ^ "BrowserSpy". gemal.dk. http://gemal.dk/browserspy/. Retrieved 2010-01-28. 
  2. ^ "IE "default behaviors [sic]" browser information disclosure tests: clientCaps". Mypage.direct.ca. http://mypage.direct.ca/s/schinke/defaultbehaviors/clientCapsExtra.html. Retrieved 2010-01-28. 
  3. ^ CyberSource Brings New Levels of Automation to Online Fraud Control[dead link]
  4. ^ "User confidence takes a Net loss". Techworld.com. 1970-01-01. http://www.techworld.com/security/features/index.cfm?featureid=1575. Retrieved 2010-01-28. 
  5. ^ a b "Wireless Device Driver Fingerprinting" (PDF). http://www.sandia.gov/news/resources/releases/2006/images/wireless-fingerprinting.pdf. Retrieved 2010-01-28. 
  6. ^ "Remote Physical Device Detection". Cs.washington.edu. http://www.cs.washington.edu/homes/yoshi/papers/PDF/. Retrieved 2010-01-28. 
  7. ^ "Chatter on the Wire: A look at DHCP traffic" (PDF). http://myweb.cableone.net/xnih/download/chatter-dhcp.pdf. Retrieved 2010-01-28. 
  8. ^ TCP/IP stack fingerprinting
  9. ^ "Chatter on the Wire: A look at excessive network traffic and what it can mean to network security." (PDF). http://myweb.cableone.net/xnih/download/OS%20FingerPrint.pdf. Retrieved 2010-01-28. 
  10. ^ "EFF's Top 12 Ways to Protect Your Online Privacy | Electronic Frontier Foundation". Eff.org. 2002-04-10. http://www.eff.org/wp/effs-top-12-ways-protect-your-online-privacy. Retrieved 2010-01-28. 
  11. ^ "MSIE clientCaps "isComponentInstalled" and "getComponentVersion" registry information leakage". Archive.cert.uni-stuttgart.de. http://archive.cert.uni-stuttgart.de/bugtraq/2003/11/msg00031.html. Retrieved 2010-01-28. 

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