Prior research has shown that public vulnerability systems such as US National Vulnerability Database (NVD) rely on a manual, time-consuming, and error-prone process which has led to inconsistencies and delays in releasing final vulnerability results. This work provides an approach to curate vulnerability reports in real-time and map textual vulnerability reports to machine readable structured vulnerability attribute data. Designed to support the time consuming human analysis done by vulnerability databases, the system leverages the Common Vulnerabilities and Exposures (CVE) list of vulnerabilities and the vulnerability attributes described by the National Institute of Standards and Technology (NIST) Vulnerability Description Ontology (VDO) framework. Our work uses Natural Language Processing (NLP), Machine Learning (ML) and novel Information Theoretical (IT) methods to provide automated techniques for near real-time publishing, and characterization of vulnerabilities using 28 attributes in 5 domains. Experiment results indicate that vulnerabilities can be evaluated up to 95 hours earlier than using manual methods, they can be characterized with F-Measure values over 0.9, and the proposed automated approach could save up to 47% of the time spent for CVE characterization.

In the summer of 2011, we revamped the curriculum for the GLITCH Game Testers research project to better serve the interests of the 15 student participants. Our new curriculum, based on Greenfoot and game development, replaced an earlier curriculum that students felt was inauthentic. Through Greenfoot, the new curriculum had the benefits of authenticity, easy access to concepts that were relevant to the students, and immediate visual feedback, and these qualities helped align students' perceptions with their actual abilities to program based on what they had learned. We analyze students' reported abilities and their demonstrated abilities and show how the two align, and we suggest long-term implications for maintaining that alignment.