EAGER: SaTC-EDU: Training Mid-Career Security Professionals in Machine Learning and Data-Driven Cybersecurity

网络安全和机器学习(ML)领域的发展相对独立。这两个领域之间的偶尔重叠通常采取(1)将ML应用于统计异常检测(例如，恶意软件检测)的形式；或(2)对ML检测算法的对抗性攻击(例如，对抗性ML)。网络安全和ML领域也在迅速发展，这使得在这两个领域以及在它们的交叉点上的教育都变得至关重要。通过数据驱动和ML方法的大规模在线培训来提升和重新掌握美国网络安全劳动力的技能，对于保持国家安全和劳动力竞争力至关重要。项目组将通过为职业生涯中期的安全专业人员开发大规模在线培训课程来满足这一迫切需求，这些专业人员的目标是发展将传统和尖端ML工具应用于网络安全的技能。该项目将在ML和网络安全的交叉点上开发课程，重点是将ML应用于实际的、真实世界的安全用例。此外，该项目将为安全研究人员建立一个教学基础，以评估和应用各种潜在的基于ML的网络安全方法。该项目尤其侧重于培训职业生涯中期的专业人员，他们接受过网络安全方面的经典培训(因此对实用概念有一定的理解)，但需要在数据驱动的方法方面获得更坚实的基础，这些方法在过去十年中已成为大多数应用的网络安全的基础。项目成果将包括：(1)开发数据驱动安全的在线课程，为职业生涯中期的专业人员提供将这些方法应用于网络安全实际问题的基础和实用工具；(2)形成研究，从工作人员那里获得所需的技能和用例；(3)在两个课程中使用的模块化公共工具包和数据集，作为专业人员在实际环境中应用的资源；以及(4)基于智能教学系统为学生个体量身定做的增强型教材。该项目由安全和值得信赖的网络空间(SATC)计划的一项特别倡议支持，旨在促进网络安全、人工智能和教育领域之间新的、以前从未探索过的合作。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。

The cybersecurity and machine learning (ML) fields have evolved relatively independently. The occasional overlap between the two fields generally takes the form of either (1) applications of ML to statistical anomaly detection (e.g., malware detection); or (2) adversarial attacks on ML detection algorithms (e.g., adversarial ML). The cybersecurity and ML fields are also rapidly advancing, which makes education both in these respective fields and at their intersection critical. Advancement and re-skilling the United States cybersecurity workforce through large-scale, online training in data-driven and ML methods is critical for keeping the country secure and the workforce competitive. The project team will address this critical need by developing curricula for large-scale, online training of mid-career security professionals who aim to develop the skills to apply both conventional and cutting-edge ML tools to cybersecurity. This project will develop curricula at the intersection of ML and cybersecurity with a focus on applications of ML to practical, real-world security use cases. In addition, the project will establish a pedagogical foundation for security researchers to evaluate and apply various potential ML-based approaches to cybersecurity. The project is focused, in particular, on training mid-career professionals who have a classical training in cybersecurity (and thus an understanding of practical concepts), but need to gain a stronger foundation in data-driven methods that have become the basis for most applied cybersecurity in the past decade. The project outcomes will include: (1) online curricular development in data-driven security, to provide mid-career professionals foundations and practical tools for applying these methods to practical problems in network security; (2) formative research to elicit desired skills and use cases from the workforce; (3) modular public toolkits and datasets for use in both courses and as resources for professionals to apply in practical settings; and (4) augmented teaching materials, tailored to individual students, based on intelligent tutoring systems.This project is supported by a special initiative of the Secure and Trustworthy Cyberspace (SaTC) program to foster new, previously unexplored, collaborations between the fields of cybersecurity, artificial intelligence, and 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.