1. Team Members: Ana Caicedo Escobar Sandeep Indukuri Deepthi Tulasi Kevin Chan Under Esteemed Guidance of: Prof. Charles C Tappert Robert Zack

2. Keystroke Biometrics System Data/Features Keystroke biometric systems measure typing characteristics believed to be unique to an individual and difficult to duplicate. The keystroke biometric has several possible applications. 1. identify an individual from his/her keystroke pattern (one-of-n response) 2. authentication process (accept/reject response)

3. Improve the feature extractor program to obtain 239 features instead of 230 and evaluate accuracy Investigate the use of key loggers to capture keystroke data Conduct weak and strong training experiments Work onto efficient authentication processing Implementation

4. 239 Features Improvement The 239 features are grouped as follows: 1. 78 duration features 2. 70 type-1 transition features 3. 70 type-2 transition features 4. 19 percentage 5. 2 keystroke input-rates: Unadjusted input-rate Adjusted input-rate In recent experiments the program was missing 9 feature measurements

5. Code was included on the following Java Applets: Linguistic.java FeatureExtractor.java Fallback.java KeyFeature.java Java Code

13. 239 Improvement Before and After

14. Accuracy with 239 Key Features

15. Improvement in FAR and FRR

16. Key logger is a program that records all keystrokes. Basic Key Logger by Eric J. Fimbel This application built with Python is open source software This key logger can be modified to get a keystroke raw data file Key Logger

17. Key Logger that captures time stamp in milliseconds. Key Logger

18. The term “weak training” means that different subjects are used for training and testing. For testing we used the recent 2009-2010 18-subject data. For training we used the 18-subject training data used in the baseline (original weak training) experiment Weak Training

19. Weak Training Experiment Results

20. Strong training uses 1. same data for training 2. different data samples for testing We used the recent 2009-2010 data For each subject, 5 examples were entered in one time period and 5 examples at least two weeks later. The earlier data were used for training and the later for testing Strong Training Experiment

21. Strong Training Experiment Results

22. Difference between Strong and Weak Performance: 99.05%--Strong 95.32%---Weak FAR:.47%--Strong 3.65%---Weak FRR: 11.11%--Strong 24.20%---Weak

23. Apply the same x-min and x-max values to the testing data that were obtained from the training data Add new options to the Feature Extractor 1.Option would create a file with x-min/x-max values using the raw training data 2. Option would read this file and use the x-min and x-max values to perform the standardization on the testing data Authentication Procedure Authentication Procedure

24. Efficient Authentication Procedure

26. Improvement of 230 features to 239 Decrease the 1NN error rate from 4.03% to 2.67% Performance Increase of Authentication System Key Logger capture of key entry operations in applications Feature Extractor to create a file with the x- min/x-max values Conclusion

28. Thank you