BioType is a behavioral biometrics WebDNA function based on ADGS research and development (from version 8.5)BioType is based on the concept of behavioral biometrics, meaning the way people do things individually, such as speaking, writing, typing, walking styles.
There are three methods:
- initialize records the keystroke dynamic for a specific user. He will have to type his credentials from 2 to 4 times to get enough data to build the profile.
- evaluate Once the profile is built and stored for a user, [evaluate] will compare every new credential typing and return a value from 0.1 to 4.0
0.1 to 0.5 is an evaluation that can be translated as "almost certainly the same user" (>90% chances)
0.5 to 0.8 is probably the same user (80% to 90%)
0.8 to 4.0 is most probably an impostor
- train may be used by the administrator from time to time to rebuild the profile, if the initial conditions change: different keyboard layout, health or physical problems etc...
|btuser||Name of the BioType user, ignored for demo|
|bttype||Set to "TEXT" for large blocks of text, otherwise defaults to password. This controls how BioType evaluates the keystrokes|
|btcorrections||Maximum number of corrections allowed, defaults to 1|
|btlength||Number of keystrokes for the text/password, defaults to 8|
|btthreshold||This parameter allows to specify the [user_deviation] value for which a user will be considered "impostor". Anything above it triggers the imposter. Below is "legitimate". The result is shown with [biotype]|
When running "evaluate", if the result is "LEGITIMATE" then afterwards BioType will train with the data: this means that the data will automatically and transparently be used to train the system for this user. If the result is "IMPOSTER", then it will not train or modify the database for the user.
The performance of the authentication method is measured using two error rate metrics: False Acceptance Rate (probability that an impostor is accepted as a legitimate user) and False Rejection Rate (probability that the legitimate user is rejected by the system)
The asymptotical average error rates are 0.92% for FAR (with a standard deviation of 0.74) and 4.28% for FRR (with a standard deviation of 2.73); both distributions are positively skewed. Almost optimal error rates are achieved approximately after 50 sessions or 15 thousand effective keystrokes and error rates double that figure are achieved after half the sessions or effective keystroke count.
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