Capacity Building: Integrating Data Science into Cybersecurity Curriculum

博伊西州立大学（BSU）是指定的网络防御教育学术卓越中心，该项目建议提高美国高等教育企业培养具有分析技能的安全数据科学家和网络安全专业人员的能力。该项目旨在通过将数据科学整合到网络安全课程中，建立安全数据科学教育的能力。该项目旨在解决安全数据分析课程材料的缺乏，以及缺乏足够资格教授安全数据科学的教师。这些材料将融合数据科学的过程、安全问题、对手的观点和学术课程设计的原则。通过开放式协作存储库、教师发展研讨会和网络研讨会、专业会议上的演讲和教程进行传播，将为全国各地的教育工作者开辟途径，帮助他们将数据科学主题整合到网络安全课程中。该项目的第一个目标是通过将数据科学工作流程、安全和隐私问题、对手的安全观点以及基于探究的学习整合到实践中，开发安全数据科学方面的创新课程材料。将创建四个独立的课程模块，涵盖软件漏洞预测的数据科学、社交网络中的恶意用户检测、恶意软件检测和入侵检测。这些模块可以用于现有的网络安全课程，如软件安全和网络安全，或者结合起来创建一个新的安全数据分析的独立课程。第二个目标是为安全数据科学社区开发一个开放的协作存储库，以托管拟议的课程材料（例如，课堂讲稿、数据集、工具和实验练习），并为社区提供一个协作环境，供教育工作者交换想法、贡献新内容和共享资源。第三个目标是举办关于安全数据科学的全国性教师发展研讨会。研讨会将汇集各种利益相关者，如研究人员、教育工作者、行业从业者和政策制定者，讨论安全数据科学的最新进展和需求，以及如何将数据科学整合到具体的安全课程中。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The project from Boise State University (BSU), a designated Center of Academic Excellence in Cyber Defense Education, proposes to increase the capacity of the United States higher education enterprise to produce security data scientists and cybersecurity professionals with analytic skills. This project seeks to build a capacity in security data science education by integrating data science into a cybersecurity curriculum. This project seeks to address the lack in curriculum materials on security data analytics, and the shortage of faculty that are sufficiently qualified to teach security data science. The materials will blend processes in data sciences, security problems, perspectives of adversaries, and principles of academic course design. Dissemination via an open collaborative repository, faculty development workshops and webinars, presentations and tutorials at professional conferences will open-up pathways for educators around the nation to help them integrate data science topics into their cybersecurity curriculum.The first objective of the project is to develop innovative curriculum materials on security data science by integrating data science workflow, security and privacy problems, the adversary's perspective of security, and inquiry-based learning into hands-on practices. Four self-contained course modules will be created, covering data science for software vulnerability prediction, malicious user detection in social networks, malware detection, and intrusion detection. These modules can be used in existing cybersecurity courses, such as Software Security and Network Security or combined to create a new standalone course on security data analytics. The second objective is to develop an open collaborative repository for the security data science community to host the proposed curriculum materials (e.g., lecture notes, data sets, tools, and lab exercises), and serve the community as a collaborative environment for educators to exchange ideas, contribute new contents, and share resources. The third goal is to hold national faculty development workshops on security data science. The workshops will bring together various stakeholders, such as researchers, educators, industry practitioners, and policy makers, to discuss most recent progresses and needs in security data science and how to integrate data science into specific security courses.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.