NSF Award
2100115
This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).<br/><br/>As cybersecurity threats grow in complexity, the burden of responding to these threats also increases. Early detection of security vulnerabilities and threats is needed. Machine learning (ML) approaches enable the analysis of large amounts of data and could be used to predict and prevent future cybersecurity threats. This project will enhance the cybersecurity curricula across computing disciplines using an authentic learning approach. Authentic learning approaches engage students’ active learning and problem-solving capabilities by using hands-on approaches and real-world topics. This approach has been increasingly popular for teaching cybersecurity but is less commonly used to teach ML in cybersecurity. The project will design and develop ten portable labware modules that will support a broad audience to learn ML in cybersecurity effectively and result in more efficient student engagement. The resources developed will support authentic learning of cybersecurity topics, and increase student learning and interests as well as faculty collaboration between Kennesaw State University and Tuskegee University. The project will disseminate the resources via faculty workshops, conference publications, and webinars.<br/><br/>The design of the proposed learning modules will be based on popular machine learning algorithms and publicly available free datasets related to common cybersecurity problems such as Denial of Service, CAPTCHA bypassing, and SQL Injection attacks. The modules will be deployed on the open-source Google CoLaboratory (CoLab) environment. Learners will access and practice all labs interactively using a browser anywhere and anytime without a need for time-consuming installation and configuration. The hands-on labs will provide students with step-by-step interactive activities to learn ML models in the CoLab environment, followed by testing of models. The project will seek to answer the following research questions: (i) Do innovative, authentic learning-based ML in cybersecurity resources increase learners’ knowledge and interest in solving real-world problems and careers in the cybersecurity workforce? (ii) Does the hands-on labware developed by the project impact students’ grades, learning, attitudes, motivation, and self-efficacy towards ML in cybersecurity? (iii) What is the relationship between students’ motivation and ML in cybersecurity learning? (iv) Do participating faculty perceive the ML in cybersecurity authentic learning resources as effective in engaging diverse, underrepresented students in cybersecurity? The project evaluation will use a mixed-methods design and administrative data, focus groups, and survey data. The quantitative and qualitative data generated from these sources will be used for formative and summative assessments. <br/><br/>This project is supported by the Secure and Trustworthy Cyberspace (SaTC) program, which funds proposals that address cybersecurity and privacy, and in this case specifically cybersecurity education. The SaTC program aligns with the Federal Cybersecurity Research and Development Strategic Plan and the National Privacy Research Strategy to protect and preserve the growing social and economic benefits of cyber systems while ensuring security and privacy.<br/><br/>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.
