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Full-Text Articles in Physical Sciences and Mathematics

A Targeted Study On The Match Between Cybersecurity Higher Education Offerings And Workforce Needs, Diane Murphy, Nektaria Tryfona, Andrew M. Marshall Apr 2023

A Targeted Study On The Match Between Cybersecurity Higher Education Offerings And Workforce Needs, Diane Murphy, Nektaria Tryfona, Andrew M. Marshall

Virginia Journal of Science

The Cybersecurity Workforce Gap is a call to action on a two-fold problem: the worldwide shortage of qualified cybersecurity workers and the need to develop a growing highly-knowledgeable, agile, well-trained cybersecurity workforce. This paper presents a methodological approach to achieve this goal in the Northern Virginia area. The area is characterized by an abundance of cyber-related industries, government agencies, and large businesses with high demand of skilled cybersecurity workers; at the same time, academic institutions offer cutting edge education and training access to highly capable students. Central to this methodology is the collaboration between local academia and industry and it …


A Survey On Securing Personally Identifiable Information On Smartphones, Dar’Rell Pope, Yen-Hung (Frank) Hu, Mary Ann Hoppa Oct 2020

A Survey On Securing Personally Identifiable Information On Smartphones, Dar’Rell Pope, Yen-Hung (Frank) Hu, Mary Ann Hoppa

Virginia Journal of Science

With an ever-increasing footprint, already topping 3 billion devices, smartphones have become a huge cybersecurity concern. The portability of smartphones makes them convenient for users to access and store personally identifiable information (PII); this also makes them a popular target for hackers. This survey shares practical insights derived from analyzing 16 real-life case studies that exemplify: the vulnerabilities that leave smartphones open to cybersecurity attacks; the mechanisms and attack vectors typically used to steal PII from smartphones; the potential impact of PII breaches upon all parties involved; and recommended defenses to help prevent future PII losses. The contribution of this …


A Study Of Existing Cross-Site Scripting Detection And Prevention Techniques Using Xampp And Virtualbox, Jalen Mack, Yen-Hung (Frank) Hu, Mary Ann Hoppa Oct 2019

A Study Of Existing Cross-Site Scripting Detection And Prevention Techniques Using Xampp And Virtualbox, Jalen Mack, Yen-Hung (Frank) Hu, Mary Ann Hoppa

Virginia Journal of Science

Most operating websites experience a cyber-attack at some point. Cross-site Scripting (XSS) attacks are cited as the top website risk. More than 60 percent of web applications are vulnerable to them, and they ultimately are responsible for over 30 percent of all web application attacks. XSS attacks are complicated, and they often are used in conjunction with social engineering techniques to cause even more damage. Although prevention techniques exist, hackers still find points of vulnerability to launch their attacks. This project explored what XSS attacks are, examples of popular attacks, and ways to detect and prevent them. Using knowledge gained …


A Comparative Study On Machine Learning Algorithms For Network Defense, Abdinur Ali, Yen-Hung Hu, Chung-Chu (George) Hsieh, Mushtaq Khan Oct 2017

A Comparative Study On Machine Learning Algorithms For Network Defense, Abdinur Ali, Yen-Hung Hu, Chung-Chu (George) Hsieh, Mushtaq Khan

Virginia Journal of Science

Network security specialists use machine learning algorithms to detect computer network attacks and prevent unauthorized access to their networks. Traditionally, signature and anomaly detection techniques have been used for network defense. However, detection techniques must adapt to keep pace with continuously changing security attacks. Therefore, machine learning algorithms always learn from experience and are appropriate tools for this adaptation. In this paper, ten machine learning algorithms were trained with the KDD99 dataset with labels, then they were tested with different dataset without labels. The researchers investigate the speed and the efficiency of these machine learning algorithms in terms of several …


Section Abstracts: Computer Science Apr 2016

Section Abstracts: Computer Science

Virginia Journal of Science

Abstracts of the Computer Science Section for the 94th Annual Virginia Academy of Science Meeting, May 18-20, 2016, at University of Mary Washington, Fredericksburg, VA.


Section Abstracts: Computer Science May 2015

Section Abstracts: Computer Science

Virginia Journal of Science

Abstracts of the Computer Science Section for the 93rd Annual Meeting of the Virginia Academy of Science, May 21-23, 2015, James Madison University, Richmond, Virginia


Section Abstracts: Computer Science Apr 2014

Section Abstracts: Computer Science

Virginia Journal of Science

Abstracts of the Computer Science Section for the 92nd Annual Meeting of the Virginia Academy of Science, May 13-15, 2014, Virginia Commonwealth University, Richmond, Virginia


Section Abstracts: Computer Science May 2013

Section Abstracts: Computer Science

Virginia Journal of Science

Abstracts of the Computer Science Section for the 91st Annual Virginia Journal of Science Meeting, May 2013


Section Abstracts: Computer Science Apr 2012

Section Abstracts: Computer Science

Virginia Journal of Science

Abstracts of the Computer Science Section for the 90th Annual Meeting of the Virginia Academy of Science, May 23-25, 2012, Norfolk State University, Norfolk, Virginia.


2011 Meeting Missing Abstracts Oct 2011

2011 Meeting Missing Abstracts

Virginia Journal of Science

Abstracts not included in Virginia Journal of Science Volume 62, No. 1 and 2, presented at the 89th Annual Meeting of the Virginia Academy of Science, May 25-27, 2011, at the University of Richmond, Richmond VA.


Section Abstracts: Computer Science Apr 2011

Section Abstracts: Computer Science

Virginia Journal of Science

Abstracts of the Computer Sciences Section for the 89th Annual Meeting of the Virginia Academy of Science, May 25-27, 2011, University of Richmond, Richmond VA.


Section Abstracts: Computer Science Apr 2010

Section Abstracts: Computer Science

Virginia Journal of Science

Abstracts of the Computer Sciences Section for the 88th Annual Meeting of the Virginia Academy of Science, May 20-21, 2010, James Madison University, Harrisonburg, VA.


Section Abstracts: Computer Science Jul 2009

Section Abstracts: Computer Science

Virginia Journal of Science

Abstracts of the Computer Science for the 87th Annual Meeting of the Virginia Academy of Science, May 27-29, 2009, Virginia Commonwealth University, Richmond Virginia.


New Fellow: Robert A. Willis, Jr. Apr 2006

New Fellow: Robert A. Willis, Jr.

Virginia Journal of Science

Robert A. Willis, Jr. has been named Fellow of the Virginia Academy of Science. He has been an active member of the Virginia Academy of Science and the Association of Departments of Computer, Information Science/Engineering at Minority Institutions (ADMI) for nearly fifteen years.


Three-Dimensional Reconstructions Of Tadpole Chondrocrania From Historical Sections, Gary Radice, Mary K. Boggiano, Mark Desantis, Peter Larson, Joseph Oppong, Matthew Smetanick, Todd Stevens, James Tripp, Rebecca Weber, Michael Kerckhove, Rafael De Sá Oct 1999

Three-Dimensional Reconstructions Of Tadpole Chondrocrania From Historical Sections, Gary Radice, Mary K. Boggiano, Mark Desantis, Peter Larson, Joseph Oppong, Matthew Smetanick, Todd Stevens, James Tripp, Rebecca Weber, Michael Kerckhove, Rafael De Sá

Virginia Journal of Science

Reconstructing three dimensional structures (3DR) from histological sections has always been difficult but is becoming more accessible with the assistance of digital imaging. We sought to assemble a low cost system using readily available hardware and software to generate 3DR for a study of tadpole chondrocrania. We found that a combination of RGB can1era, stereomicro­scope, and Apple Macintosh PowerPC computers running NIH Image, Object Image, Rotater, and SURFdriver software provided acceptable reconstruc­tions. These are limited in quality primarily by the distortions arising from histological protocols rather than hardware or software.