Collaborative Research: SaTC: EDU: Authentic Learning of Machine Learning in Cybersecurity with Portable Hands-on Labware

该奖项的全部或部分资金来自《2021年美国救援计划法案》(公法117-2)。随着网络安全威胁的复杂性增加，应对这些威胁的负担也增加了。需要及早发现安全漏洞和威胁。机器学习(ML)方法能够分析大量数据，并可用于预测和预防未来的网络安全威胁。该项目将使用可信的学习方法，加强计算机学科的网络安全课程。真正的学习方法通过使用动手方法和现实世界的主题来激发学生的主动学习和解决问题的能力。这种方法在教授网络安全方面越来越受欢迎，但在教授ML网络安全方面却不太常用。该项目将设计和开发十个便携式实验室模块，以支持广泛的受众有效地学习网络安全中的ML，并导致更有效的学生参与。开发的资源将支持网络安全主题的真实学习，并增加学生的学习和兴趣以及肯纳索州立大学和塔斯基吉大学之间的教师合作。该项目将通过教师研讨会、会议出版物和网络研讨会传播资源。拟议学习模块的设计将基于流行的机器学习算法和公开可用的免费数据集，这些数据集与常见的网络安全问题有关，如拒绝服务、绕过验证码和SQL注入攻击。这些模块将部署在开源的谷歌合作实验室(CoLab)环境中。学员将随时随地使用浏览器以交互方式访问和练习所有实验，无需进行耗时的安装和配置。动手实验将为学生提供在CoLab环境中学习ML模型的循序渐进的互动活动，然后测试模型。该项目将寻求回答以下研究问题：(I)网络安全资源中创新的、真实的基于学习的ML是否提高了学习者解决现实世界问题的知识和兴趣，以及在网络安全劳动力中的职业？(Ii)该项目开发的动手实验室软件是否影响学生在网络安全领域对ML的成绩、学习、态度、动机和自我效能感？(3)学生在网络安全学习中的学习动机与学习记忆能力之间存在什么关系？(Iv)参与课程的教员是否认为ML网络安全专业的真实学习资源能有效地让不同、代表性不足的学生参与网络安全课程？项目评估将使用混合方法设计和管理数据、焦点小组和调查数据。从这些来源产生的定量和定性数据将用于形成性和总结性评估。这个项目得到了安全和值得信赖的网络空间(SATC)计划的支持，该计划为解决网络安全和隐私问题的提案提供资金，在这种情况下，特别是网络安全教育。SATC计划与联邦网络安全研究和发展战略计划和国家隐私研究战略保持一致，以保护和维护网络系统日益增长的社会和经济效益，同时确保安全和隐私。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).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.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. 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.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.