Biblio

The last decade has witnessed an increase in online human interactions, covering all aspects of personal and professional activities. Identification of people based on their behavior rather than physical traits is a growing industry, spanning diverse spheres such as online education, e-commerce and cyber security. One prominent behavior is the expression of opinions, commonly as a reaction to images posted online. Visual aesthetic is a soft, behavioral biometric that refers to a person's sense of fondness to a certain image. Identifying individuals using their visual aesthetics as discriminatory features is an emerging domain of research. This paper introduces a new method for aesthetic feature dimensionality reduction using gene expression programming. The advantage of this method is that the resulting system is capable of using a tree-based genetic approach for feature recombination. Reducing feature dimensionality improves classifier accuracy, reduces computation runtime, and minimizes required storage. The results obtained on a dataset of 200 Flickr users evaluating 40000 images demonstrates a 94% accuracy of identity recognition based solely on users' aesthetic preferences. This outperforms the best-known method by 13.5%.

Free text keystroke dynamics is a behavioral biometric that has the strong potential to offer unobtrusive and continuous user authentication. Unfortunately, due to the limited data availability, free text keystroke dynamics have not been tested adequately. Based on a novel large dataset of free text keystrokes from our ongoing data collection using behavior in natural settings, we present the first study to evaluate keystroke dynamics while respecting the temporal order of the data. Specifically, we evaluate the performance of different ways of forming a test sample using sessions, as well as a form of continuous authentication that is based on a sliding window on the keystroke time series. Instead of accumulating a new test sample of keystrokes, we update the previous sample with keystrokes that occur in the immediate past sliding window of n minutes. We evaluate sliding windows of 1 to 5, 10, and 30 minutes. Our best performer using a sliding window of 1 minute, achieves an FAR of 1% and an FRR of 11.5%. Lastly, we evaluate the sensitivity of the keystroke dynamics algorithm to short quick insider attacks that last only several minutes, by artificially injecting different portions of impostor keystrokes into the genuine test samples. For example, the evaluated algorithm is found to be able to detect insider attacks that last 2.5 minutes or longer, with a probability of 98.4%.

In this paper, an innovative approach to keyboard user monitoring (authentication), using keyboard dynamics and founded on the concept of time series analysis, is presented. The work is motivated by the need for robust authentication mechanisms in the context of on-line assessment such as those featured in many online learning platforms. Four analysis mechanisms are considered: analysis of keystroke time series in their raw form (without any translation), analysis consequent to translating the time series into a more compact form using either the Discrete Fourier Transform or the Discrete Wavelet Transform, and a "benchmark" feature vector representation of the form typically used in previous related work. All four mechanisms are fully described and evaluated. A best authentication accuracy of 99% was obtained using the wavelet transform.

Free-text keystroke authentication has been demonstrated to be a promising behavioral biometric. But unlike physiological traits such as fingerprints, in free-text keystroke authentication, there is no natural way to identify what makes a sample. It remains an open problem as to how much keystroke data are necessary for achieving acceptable authentication performance. Using public datasets and two existing algorithms, we conduct two experiments to investigate the effect of the reference profile size and test sample size on False Alarm Rate (FAR) and Imposter Pass Rate (IPR). We find that (1) larger reference profiles will drive down both IPR and FAR values, provided that the test samples are large enough, and (2) larger test samples have no obvious effect on IPR, regardless of the reference profile size. We discuss the practical implication of our findings.