The Advanced Research Computing in the Humanities Engineering and Sciences (ARCHES) network facilitates research in fields of computational inquiry by helping address the continuously evolving challenges of managing scientific data. The main features of the project include (a) deploying an aggregation router to improve connectivity to Internet 2; (b) enhancing the current data transport capabilities around campus laboratories, computational clusters and the digital humanities media laboratory; (c) deploying perfSONAR to monitor network traffic; and (d) assess the impact of cyberinfrastructure in building novel collaborations between scientific research and educational groups. The ARCHES network design improves on successful implementations of similar networks and finds an appropriate balance to simultaneously address network security, research, and educational needs. The network enables educators to adopt new pedagogical approaches and technologies, and to provide students with an enhanced educational experience. With its emphasis on workforce development, ARCHES provides pathways for development of new curricular material and supports research experiences for undergraduate students and schoolteachers. The network provides new opportunities for researchers, students, and educators alike, thus having a significant impact on a number of research domains.

This project makes two main contributions to cybersecurity education: the development of a specification-driven, dynamic environment for implementing realistic cyber defense and forensic analysis exercises; and the advanced support for class management and automated evaluation. The platform, AutoCUE, provides a high-level specification language, and an execution runtime that enable instructors to easily and efficiently run realistic scenarios that result in customized environments; based on the same methods, the system also be used to automatically create of realistic experimental data sets. The infrastructure provides an automated class management component, which consists of: a) deployment automation module, which guarantees consistent student lab environment, and central control by the instructor; b) scenario personalization module, which can generate customized exercises for each student (for evaluation purposes); and c) automated grading module, which combines ideas from capture-the-flag competitions and environment sensors to track student progress and automate the grading process. The project also provides ready-to-use seed content for two classes: digital forensics and network penetration testing.

This project evaluates the effectiveness of peer instruction methodology for cybersecurity education, and develops the peer instruction material for three cybersecurity courses offering an introduction to security concepts, a defensive view of cybersecurity, and an offensive view of cybersecurity. The two primary mechanisms are used for measuring impact of peer instruction, which are pre- and post-testing, and isomorphic questions.

The goal of this project is to develop a lightweight infrastructure for supporting hands-on network security education (NSE) and a compelling set of exercises that rely on the infrastructure, covering the three basic aspects of the security: attack, analysis, and defense. Historically, building realistic Cybersecurity exercises has been both a laborious and resource-intensive task. Over the course of a semester, the number of virtual machines (VM) and snapshots on the server can easily become hundreds or thousands, which imposes serious maintenance overhead on the instructor and systems administration staff Finally, once created, the VMs are relatively large and are not easily shareable across the Internet, thereby effectively limiting the exchange of materials among educators. The project proposes a solution to fundamentally change how Cybersecurity exercises are created and delivered by creating a flexible Cybersecurity education infrastructure based on lightweight virtualization containers. The Secure and Trustworthy Cyberspace (SaTC) program funds proposals that address Cybersecurity from a Trustworthy Computing Systems perspective; a Social, Behavioral and Economic Sciences perspective; and proposals focusing entirely on Cybersecurity Education.