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Adult content on the Internet may be accessed by children with only a few keystrokes. While separate child-safe accounts may be established, a better approach could be incorporating automatic age estimation capability into the browser. We envision a safer browsing experience by implementing child-safe browsers combined with Internet content rating similar to the film industry. Before such a browser is created it was necessary to test the age estimation module to see whether acceptable error rates are possible. We created an Android application for collecting biometric touch data, specifically tapping data. We arranged with an elementary school, a middle school, a high school, and a university and collected samples from 262 user sessions (ages 5 to 61). From the tapping data, feature vectors were constructed, which were used to train and test 14 regressors and classifiers. Results for regression show the best mean absolute errors of 3.451 and 3.027 years, respectively, for phones and tablets. Results for classification show the best accuracies of 73.63% and 82.28%, respectively, for phones and tablets. These results demonstrate that age estimation, and hence, a child-safe browser, is feasible, and is a worthwhile objective. © 2020 IEEE.
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The internet has changed the way that many people access written works. Books and articles, of various lengths, in several formats can be bought and accessed online, both legally and illegally. Texts in even shorter form are originating through forums, SMS, blogs, emails, and social media. Automating the process of determining the authorship of posted texts would help combat online piracy of copyrighted text and plagiarism. In addition, authorship identification could help detect fraudulent email messages from dangerous sources and combat cyberattacks by identifying authentic sources. We experiment with several machine learning algorithms on a limited set of public domain literature to identify the most efficient method of authorship identification using the least amount of samples. Different sized data sets are created by 5 predefined rounds of random sampling of 1500 word blocks on a total of 28 text books from a corpus of 7 authors. Traditional methods of authorship identification, such as Naive Bayes, Artificial Neural Network, and Support Vector Machine are implemented in addition to using a modern Deep Learning Neural Network for classification. Thirteen stylometric features are extracted ranging from character based, word based, and syntactic features. Our model consistently showed that Support Vector Machine out performs other classification methods. © 2020
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The cyber-behavioral biometric modalities such as keystroke dynamics, mouse dynamics, and touch screen dynamics have come under attacks of different forms in recent days. To address these attacks and other security issues, we present a novel concept of using smartwatch sensor data to continuously verify users in cyberspace and show its potential to be a new standalone cyber-behavioral biometric modality. For our experiments, smartwatch gyroscope and accelerometer data collected from 49 subjects while typing in desktop computer have been considered. We implemented six pattern matching classifiers to compare each verification attempt against the user profile. Experimental results comprising of 282, 240 classification attempts show significantly high True Positive (TP) rates and extremely low False Positive (FP) rates with the highest achieved TP rate of 87.2% and lowest FP rate of 0.2%. With this level of accuracy and natural resiliency to attacks comes with physical biometric property as such in hand movement, we opine that smartwatch movement dynamics, besides being a new biometric trait, can be a solution to the security loopholes in existing cyber-behavioral biometric modalities for continuous verification. © 2020 IEEE.
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Traditional keyboards remain the input device of choice for typing-heavy environments. When attached to sensitive data, security is a major concern. To continuously authenticate users in these environments, use of keystroke dynamics can be a preferred choice. An integral part of user enrollment in a keystroke based continuous authentication system is the writing instruction (prompt) given to the users, to use as a basis for their improvised writing. There are many prompts possible, and they directly impact the performance of authentication systems. Hence, prompts should be designed carefully, and with purpose. In this paper, we bridge the gap between cognitive psychology and computer science and attempt to influence the mental state of the users to acquire a better authentication performance. We compare two kinds of writing prompts, creative and factual, for generating reference samples. In addition, we perform two robustness tests: robustness to dissimilar writing style (e.g., creative reference and factual test) and robustness to surface (e.g., hard surface reference and soft surface test). We collect data from thirty participants in four weekly sessions. We experiment with three features: key interval, key press, and key hold latencies. We use Relative (R) measure to generate the match score between the reference and test samples. Results show that creative writing consistently performs better than the factual one. Both writing prompts perform well with dissimilar style in testing, i.e., continuous authentication is found robust to writing style. Also, we find that the surface (hard or soft) used in testing need not match that used for the reference, thus continuous authentication is also surface robust. © 2020 IEEE.
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