Seong-seob Hwang

Keystroke dynamics-based authentication for mobile devices

Recently, mobile devices are used in financial applications such as banking and stock trading. However, unlike desktops and notebook computers, a 4-digit personal identification number (PIN) is often adopted as the only security mechanism for mobile devices. Because of their limited length, PINs are vulnerable to shoulder surfing and systematic trial-and-error attacks. This paper reports the effectiveness of user authentication using keystroke dynamics-based authentication (KDA) on mobile devices. We found that a KDA system can be effective for mobile devices in terms of authentication accuracy. Use of artificial rhythms leads to even better authentication performance.

Continual retraining of keystroke dynamics based authenticator

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.

Improving authentication accuracy using artificial rhythms and cues for keystroke dynamics-based authentication

Keystroke dynamics-based authentication (KDA) is to verify a users identity using not only the password but also keystroke dynamics. With a small number of patterns available, data quality is of great importance in KDA applications. Recently, the authors have proposed to employ artificial rhythms and tempo cues to improve data quality: consistency and uniqueness of typing patterns. This paper examines whether improvement in uniqueness and consistency translates into improvement in authentication performance in real-world applications. In particular, we build various novelty detectors using typing patterns based on various strategies in which artificial rhythms and/or tempo cues are implemented. We show that artificial rhythms and tempo cues improve authentication accuracies and that they can be applicable in practical authentication systems.

Improvement of keystroke data quality through artificial rhythms and cues

Keystroke dynamics based user authentication (KDA) can achieve a relatively high performance if a fairly large number of typing patterns are available. It is almost always the case that KDA is combined with password based authentication. Users are often required to change their passwords. When a user changes ones password, however, only a handful of new patterns become available. In a mobile situation, moreover, very short passwords are used. Under such a circumstance, the quality of data becomes important. Recently, artificial rhythms and cues were proposed to improve the quality of data. In this paper, we verify the effectiveness of artificial rhythms and cues through hypotheses tests using the data from 25 users under various situations. The experimental results show that artificial rhythms increase the uniqueness while cues increase the consistency.

Improving authentication accuracy of unfamiliar passwords with pauses and cues for keystroke dynamics-based authentication

Keystroke dynamics-based authentication (KDA) is to verify a users identification using not only the password but also keystroke patterns. The authors have shown in previous research that uniqueness and consistency of keystroke patterns are important factors to authentication accuracy and that they can be improved by employing artificial rhythms and tempo cues. In this paper, we implement the pause strategy and/or auditory cues for KDA and assess their effectiveness using various novelty detectors. Experimental results show that improved uniqueness and consistency lead to enhanced authentication performance, in particular for those users with poor typing ability.

Clustering-Based Reference Set Reduction for k-Nearest Neighbor

Response Modeling is concerned with computing the likelihood of a customer to respond to a marketing campaign. A major problem encountered in response modeling is huge volume of data or patterns. The k-NN has been used in various classification problems for its simplicity and ease of implementation. However, it has not been applied to problems for which fast classification is needed since the classification time rapidly increases as the size of reference set increases. In this paper, we propose a clustering-based preprocessing step in order to reduce the size of reference set. The experimental results showed an 85% decrease in classification time without a loss of accuracy.