A review on the public benchmark databases for static keystroke dynamics <s> The Acquisition Procedure <s> Keystroke dynamics based authentication (KDA) verifies a user based on the typing pattern. During enroll, a few typing patterns are provided, which are then used to train a classifier. The typing style of a user is not expected to change. However, sometimes it does change, resulting in a high false reject. In order to achieve a better authentication performance, we propose to continually retrain classifiers with recent login typing patterns by updating the training data set. There are two ways to update it. The moving window uses a fixed number of most recent patterns while the growing window uses all the new patterns as well as the original enroll patterns. We applied the proposed method to the real data set involving 21 users. The experimental results show that both the moving window and the growing window approach outperform the fixed window approach, which does not retrain a classifier. <s> BIB001 </s> A review on the public benchmark databases for static keystroke dynamics <s> The Acquisition Procedure <s> Keystroke dynamics is a behavioral biometrics showing a degradation of performance when used over time. This is due to the fact that the user improves his/her way of typing while using the system, therefore the test samples may be different from the initial template computed at an earlier stage. One way to bypass this problem is to use template update mechanisms. We propose in this work, new semi-supervised update mechanisms, inspired from known supervised ones. These schemes rely on the choice of two thresholds (an acceptance threshold and an update threshold) which are fixed manually depending on the performance of the system and the level of tolerance in possible inclusion of impostor data in the update template. We also propose a new evaluation scheme for update mechanisms, taking into account performance evolution over several time-sessions. Our results show an improvement of 50% in the supervised scheme and of 45% in the semi-supervised one with a configuration of the parameters chosen so that we do not accept many erroneous data. <s> BIB002 </s> A review on the public benchmark databases for static keystroke dynamics <s> The Acquisition Procedure <s> Keystroke Dynamics can be used to strengthen the user-name/password authentication system because it will not only consider the value of the password, but also how the password was typed. Keystroke Dynamics systems so far only accept passwords that are typed completely correct, i.e. without any corrections during the typing. The reason for this is that corrections during the typing result in missing features in the collected Keystroke Dynamics information. In this paper we will show however that the performance of the keystroke dynamics system is not influenced if indeed corrections are allowed during the typing of a password. We have investigated various solutions to handle these missing features and investigated how much the performance de-creases. We found that we can handle these missing features without any loss of performance, meaning that the algorithmic performance of the system does not decrease. In fact when allowing corrections, the system performance will highly increase due to the fact that there are no failures to capture. <s> BIB003
Various points can vary during the acquisition procedure of a dataset and we think they can impact its quality as well as the performance of the authentication method applied on it. Duration of the dataset acquisition. KD is a behavioral biometric modality which has an important intra-class variability over time BIB001 BIB002 . For an operational scenario, we expect to obtain better and non realistic performance on short term datasets than on long term ones. Indeed, only datasets with long term acquisition time span can be used for template update studies. Note that this information is different from the number of sessions, because several sessions can be acquired in a short amount of time. This information can be expressed in number of days between the first and last acquisition (e.g., 128 days). Are typing errors allowed? If a user does a typing mistake during the capture of the KD, the correction of the mistake changes the way of typing the text as more keystrokes are used to correct it. Thus, the captured information does not match the model at all. As the management of keystroke dynamics authentication with typing corrections is a recently opened field of research BIB003 , most acquisition tools force users to type correctly the text. If a user does a typing mistake, he/she must restart the capture process from scratch. This information can be expressed with a boolean (e.g., acquisition does not allow typing mistakes.) Is the acquisition done in a quiet and stable environment? To know the acquisition environment is also an interesting information. Once again, we expect more stable patterns in quiet environments than in disturbed ones (i.e., better performance). This can be expressed using words (e.g., noisy room with people talking, but same machine with keyboard at the same place each time).