Charles C. Tappert

Manoj Muniswamaiah, Tilak Agerwala, Charles C. Tappert

In the recent past, binary similarity measures have been applied in solving biometric identification problems, including fingerprint, handwritten character detection, and in iris image recognition. The application of the relevant measurements has also resulted in more accurate data analysis. This paper surveys the applicability of binary similarity and distance measures in various fields.

John V. Monaco, Charles C. Tappert

There are numerous opportunities for adversaries to observe user behavior remotely on the web. Additionally, keystroke biometric algorithms have advanced to the point where user identification and soft biometric trait recognition rates are commercially viable. This presents a privacy concern because masking spatial information, such as IP address, is not sufficient as users become more identifiable by their behavior. In this work, the well-known Chaum mix is generalized to a scenario in which users are separated by both space and time with the goal of preventing an observing adversary from identifying or impersonating the user. The criteria of a behavior obfuscation strategy are defined and two strategies are introduced for obfuscating typing behavior. Experimental results are obtained using publicly available keystroke data for three different types of input, including short fixed-text, long fixed-text, and long free-text. Identification accuracy is reduced by 20% with a 25 ms random keystroke delay not noticeable to the user.

John V. Monaco, Charles C. Tappert

The partially observable hidden Markov model is an extension of the hidden Markov Model in which the hidden state is conditioned on an independent Markov chain. This structure is motivated by the presence of discrete metadata, such as an event type, that may partially reveal the hidden state but itself emanates from a separate process. Such a scenario is encountered in keystroke dynamics whereby a user's typing behavior is dependent on the text that is typed. Under the assumption that the user can be in either an active or passive state of typing, the keyboard key names are event types that partially reveal the hidden state due to the presence of relatively longer time intervals between words and sentences than between letters of a word. Using five public datasets, the proposed model is shown to consistently outperform other anomaly detectors, including the standard HMM, in biometric identification and verification tasks and is generally preferred over the HMM in a Monte Carlo goodness of fit test.