Keystroke dynamics, or typing dynamics, is the detailed timing information that describes exactly when each key was pressed and when it was released as a person is typing at a computer keyboard.

The behavioral biometric of Keystroke Dynamics uses the manner and rhythm in which an individual types characters on a keyboard or keypad. The keystroke rhythms of a user are measured to develop a unique biometric template of the users typing pattern for future authentication. Raw measurements available from most every keyboard can be recorded to determine Dwell time (the time a key pressed) and Flight time (the time between “key up” and the next “key down”). The recorded keystroke timing data is then processed through a unique neural algorithm, which determines a primary pattern for future comparison. Similarly, vibration information may be used to create a pattern for future use in both identification and authentication tasks.

Data needed to analyze keystroke dynamics is obtained by keystroke logging. Normally, all that is retained when logging a typing session is the sequence of characters corresponding to the order in which keys were pressed and timing information is discarded. When reading email, the receiver cannot tell from reading the phrase "I saw 3 zebras!" whether:

• that was typed rapidly or slowly
• the sender used the left shift key, the right shift key, or the caps-lock key to make the "i" turn into a capitalized letter "I"
• the letters were all typed at the same pace, or if there was a long pause before the letter "z" or the numeral "3" while you were looking for that letter
• the sender typed any letters wrong initially and then went back and corrected them, or if he got them right the first time

Researchers are interested in using this keystroke dynamic information, which is normally discarded, to verify or even try to determine the identity of the person who is producing those keystrokes. This is often possible because some characteristics of keystroke production are as individual as handwriting or a signature. The techniques used to do this vary widely in power and sophistication, and range from statistical techniques to neural-nets to artificial intelligence.

In the simplest case, very simple rules can be used to rule out a possible user. For example, if we know that John types at 20 words per minute, and the person at the keyboard is going at 70 words per minute, it's a pretty safe bet that it's not John. That would be a test based simply on raw speed uncorrected for errors. It's only a one-way test, as it's always possible for people to go slower than normal, but it's unusual or impossible for them to go twice their normal speed.

Or, it may be that the mystery user at the keyboard and John both type at 50 words per minute; but John never really learned the numbers, and always has to slow down an extra half-second whenever a number has to be entered. If the mystery user doesn't slow down for numbers, then, again, it's a safe bet this isn't John.

The time to get to and depress a key (seek-time), and the time the key is held-down (hold-time) may be very characteristic for a person, regardless of how fast he is going overall. Most people have specific letters that take them longer to find or get to than their average seek-time over all letters, but which letters those are may vary dramatically but consistently for different people. Right-handed people may be statistically faster in getting to keys they hit with their right hand fingers than they are with their left hand fingers. Index fingers may be characteristically faster than other fingers to a degree that is consistent for a person day-to-day regardless of their overall speed that day.

In addition, sequences of letters may have characteristic properties for a person. In English, the word "the" is very common, and those three letters may be known as a rapid-fire sequence and not as just three meaningless letters hit in that order. Common endings, such as "ing", may be entered far faster than, say, the same letters in reverse order ("gni") to a degree that varies consistently by person. This consistency may hold and may reveal the person's native language's common sequences even when they are writing entirely in a different language, just as revealing as an accent might in spoken English.

Common "errors" may also be quite characteristic of a person, and there is an entire taxonomy of errors, such as this person's most common "substitutions", "reversals", "drop-outs", "double-strikes", "adjacent letter hits", "homonyms", hold-length-errors (for a shift key held down too short or too long a time). Even without knowing what language a person is working in, by looking at the rest of the text and what letters the person goes back and replaces, these errors might be detected. Again, the patterns of errors might be sufficiently different to distinguish two people.

See also:
Keystroke dynamics
Identity Theft, Computers and Behavioral Biometrics
Keystroke dynamics as a biometric for authentication

Source: Wikipedia (All text is available under the terms of the Creative Commons Attribution-ShareAlike License)