Building Cybersecurity Analytics Capacity in Big Data Era: Developing Hands-on Labs for Integrating Data Science into Cybersecurity Curriculum

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Given the wide-spread use of big data, there is a growing need to develop a cyber-workforce that understands cybersecurity in the context of big data. The goal of this project from the University of North Texas is to integrate data science into cybersecurity curriculum and train the next generation of security experts. The project proposes to have direct and long-term impacts on the growing national need for highly-trained cybersecurity professionals with data analytics capabilities, by increasing the number and quality of cybersecurity analysts. This project aims to develop instructional materials that cater to a wide-range of student learning styles. The materials will be designed so that educators at a wide-range of institutions (e.g., community college to research-intensive institutions), and with varying levels of cybersecurity knowledge, can easily incorporate them into their instruction.The proposed project seeks to develop a set of instructional modules and hands-on labs that make use of state-of-the-art data analytics for addressing different cybersecurity challenges. These instructional modules will follow active learning principles designed to engage students, regardless of learning style, and ensure that students retain the content learned. The modules will be based on real-world security systems and will be designed to systematically cover fundamental security principles. This approach will allow students to get exposure to data analytics techniques and their application to cybersecurity challenges via real-world examples. The project aims to produce engaging materials that could be easily adopted by other educators. To simplify integration and encourage adoption, the hands-on labs will be built based on only open source software and tools that are free to use for educational purposes. Further, they will be distributed via virtual machine images that already contain all libraries and required software to run the labs. This approach for development will allow a variety of instructors to confidently integrate state-of-the-art data analytics labs into curriculum with minimal effort.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.