Computational fact checking from knowledge networks

Traditional fact checking by expert journalists cannot keep up with the enormous volume of information that is now generated online. Computational fact checking may significantly enhance our ability to evaluate the veracity of dubious information. Here we show that the complexities of human fact checking can be approximated quite well by finding the shortest path between concept nodes under properly defined semantic proximity metrics on knowledge graphs. Framed as a network problem this approach is feasible with efficient computational techniques. We evaluate this approach by examining tens of thousands of claims related to history, entertainment, geography, and biographical information using a public knowledge graph extracted from Wikipedia. Statements independently known to be true consistently receive higher support via our method than do false ones. These findings represent a significant step toward scalable computational fact-checking methods that may one day mitigate the spread of harmful misinformation

Bootstrapping Trust in Online Dating: Social Verification of Online Dating Profiles

Online dating is an increasingly thriving business which boasts billion-dollar revenues and attracts users in the tens of millions. Notwithstanding its popularity, online dating is not impervious to worrisome trust and privacy concerns raised by the disclosure of potentially sensitive data as well as the exposure to self-reported (and thus potentially misrepresented) information. Nonetheless, little research has, thus far, focused on how to enhance privacy and trustworthiness. In this paper, we report on a series of semi-structured interviews involving 20 participants, and show that users are significantly concerned with the veracity of online dating profiles. To address some of these concerns, we present the user-centered design of an interface, called Certifeye, which aims to bootstrap trust in online dating profiles using existing social network data. Certifeye verifies that the information users report on their online dating profile (e.g., age, relationship status, and/or photos) matches that displayed on their own Facebook profile. Finally, we present the results of a 161-user Mechanical Turk study assessing whether our veracity-enhancing interface successfully reduced concerns in online dating users and find a statistically significant trust increase.Comment: In Proceedings of Financial Cryptography and Data Security (FC) Workshop on Usable Security (USEC), 201

Voices Raised, Issue 06

Included in this issue: Immaculate Mary; Grants augment women’s research; Mentoring grows; Women’s Studies take root in the neighborhood; Solution-oriented VP to retire; Muslim students strive to educate, support; Don’t let stress ruin your holidays; Dining services dishes up more than you’d expect; Marianist Images Across Campus; Confronting Disrespect: We Owe it to Each Other.https://ecommons.udayton.edu/wc_newsletter/1005/thumbnail.jp

Review: machine learning techniques applied to cybersecurity

Machine learning techniques are a set of mathematical models to solve high non-linearity problems of different topics: prediction, classification, data association, data conceptualization. In this work, the authors review the applications of machine learning techniques in the field of cybersecurity describing before the different classifications of the models based on (1) their structure, network-based or not, (2) their learning process, supervised or unsupervised and (3) their complexity. All the capabilities of machine learning techniques are to be regarded, but authors focus on prediction and classification, highlighting the possibilities of improving the models in order to minimize the error rates in the applications developed and available in the literature. This work presents the importance of different error criteria as the confusion matrix or mean absolute error in classification problems, and relative error in regression problems. Furthermore, special attention is paid to the application of the models in this review work. There are a wide variety of possibilities, applying these models to intrusion detection, or to detection and classification of attacks, to name a few. However, other important and innovative applications in the field of cybersecurity are presented. This work should serve as a guide for new researchers and those who want to immerse themselves in the field of machine learning techniques within cybersecurity