Identity authentication based on keystroke latencies using a genetic adaptive neural network

• US 20060242424A1
• Filed: 04/22/2005
• Published: 10/26/2006
• Est. Priority Date: 04/23/2004
• Status: Abandoned Application

• Current Assignee: Fred L. Kitchens, Queen Esther Booker, Sushil Sharma
• Original Assignee: Fred L. Kitchens, Queen Esther Booker, Sushil Sharma
• Inventors: Booker, Queen Esther, Kitchens, Fred L., Sharma, Sushil
• Application Number:
  US11/112,337
• Publication Number:
  US 20060242424A1
• Time in Patent Office: Days
• US Class Current: 713/183
• CPC Class Codes: G06F 21/316 by observing the pattern of computer usage, e.g. typical user behaviour
• CPC Class Codes: G06F 21/32 using biometric data, e.g. fingerprints, iris scans or voiceprints

Abstract

A system and method identify the person who is using a keyboard based on keystroke latencies as the person types certain key combinations. In some embodiments the latencies are monitored as the person types a password, while in others they are monitored as the person types other information and continues to use the computer. In some embodiments the identification yields a binary result (whether the latency profile matches the profile stored for a particular user), while in others a confidence level is given. A mismatch, or a confidence level below a particular threshold, results in a request for further identity verification, creation of a log entry, immediate notification of responsible personnel, or denial of access (or continued access).

Claims

1. A system, comprising a processor and a computer-readable medium, the medium being encoded with programming instructions executable by the processor to:

accept input of a password by a person via a keyboard;

capture keystroke latencies as the person enters the password, wherein the keystroke latencies are only those between characters in a predetermined set of character patterns;

apply the keystroke latencies as input to a neural network that implements a genetic algorithm; and

based on the output of the neural network, generate an authentication signal that relates to whether the person is a particular user.

2. The system of claim 1, wherein the predetermined set is enumerated in the medium.

3. The system of claim 1, wherein the predetermined set is characterized in the medium.

4. The system of claim 1, wherein the authorization signal is a binary signal.

5. The system of claim 1, wherein the authorization signal takes one among three or more values that indicate different confidence levels in the identity of the person as the user.

6. A method of maintaining computer security, comprising:

maintaining a collection of latency profiles, each for a particular authenticated user on a computer system;

monitoring the keystroke latencies as a person using the computer system types a predetermined set of n-graphs;

determining a current user as whom the person is logged in;

determining whether the keystroke latencies match the latency profile for the current user; and

if the keystroke latencies do not match the latency profile for the current user, generating an alarm signal.

7. The method of claim 6, wherein the alarm signal is a log entry.

8. The method of claim 6, wherein the alarm signal is a message to security personnel.

9. The method of claim 6, wherein the alarm signal is a denial to the user of further access to the computer system.

10. The method of claim 6, wherein the alarm signal is a request for reauthentication of the person as the user.

11. A system, including:

a keystroke latency monitor that records the latency between keystrokes that form one of a predetermined set of n-graphs of keyboard input by a person;

storage that contains a digital keystroke latency signature for a particular user;

a neural network that receives latency data from the monitor, evaluates the latency data against the digital keystroke latency signature, and provides an output signal when the variation between the latency data and the signature exceeds a predetermined threshold.

12. The system of claim 11, wherein the neural network is updated using a genetic algorithm.

13. The system of claim 11, wherein the output signal indicates that the person may be in an extreme emotional state.

14. The system of claim 11, wherein the output signal provides a warning to the person.

15. The system of claim 11, wherein the output signal is provided when the system is in an instant messaging mode.

16. The system of claim 15, wherein the output signal provides a warning to the person.

17. The system of claim 15, wherein the output signal provides a warning to the recipient of the typed text.

18. The system of claim 11, wherein the output signal is provided to the recipient of an e-mail message sent by the person.