iButton Enrolment and Verification Requirements for the Pressure Sequence Smartcard Biometric

With the growing number of smartcard applications there comes an increasing need to restrict access to the card itself. In previous work we proposed the pressure sequence biometric, within which a biometric sensor is integrated onto the card in a low-cost and mechanically compliant manner. Using an off-card verifier we demonstrated reasonable discrimination between users. In this paper we consider a number of on-card verification schemes, the best of which offers an equal error rate of 2.3%. On-card computational time requirements were found to be 3.1 seconds for enrolment and 0.12 seconds for verification. Incorporating our implementation into an existing applet used 684 bytes of program space. Whilst data memory requirements are estimated to be 1400 and 300 bytes for enrolment and verification, respectively. These time and size requirements demonstrate our biometric as a practical proposition for the protection of smart cards. Experiments were performed with the iButton's Java Card platform

Keystroke-based User Identification on Smart Phones

Abstract. Smart phones are now being used to store users ’ identities and sensitive information/data. Therefore, it is important to authenticate legitimate users of a smart phone and to block imposters. In this paper, we demonstrate that keystroke dynamics of a smart phone user can be translated into a viable feature set for accurate user identification. To this end, we collect and analyze keystroke data of 25 diverse smart phone users. Based on this analysis, we select six distinguishing keystroke features that can be used for user identification. We show that these keystroke features for different users are diffused and therefore a fuzzy classifier is well-suited to cluster and classify them. We then optimize the front-end fuzzy classifier using Particle Swarm Optimizer (PSO) and Genetic Algorithm (GA) as back-end dynamic optimizers to adapt to variations in usage patterns. Finally, we provide a novel keystroke dynamics based PIN verification mode to ensure information security on smart phones. The results of our experiments show that the proposed user identification system has an average error rate of 2 % after the detection mode and the error rate of rejecting legitimate users is dropped to zero after the PIN verification mode. We also compare error rates (in terms of detecting both legitimate users and imposters) of our proposed classifier with 5 existing state-of-the-art techniques for user identification on desktop computers. Our results show that the proposed technique consistently and considerably outperforms existing schemes.