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

EAGER：SaTC-EDU：在机器学习和数据驱动的网络安全方面培训职业中期安全专业人员

基本信息

批准号：
2041970
负责人：
Nicholas Feamster
金额：
$ 29.99万
依托单位：
University of Chicago
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-09-01 至 2023-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2041970&HistoricalAwards=false
关键词：
EAGER SaTC EDU Training Mid

项目摘要

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.

网络安全和机器学习（ML）领域相对独立地发展。这两个领域之间偶尔重叠的形式通常是（1）将机器学习应用于统计异常检测（例如恶意软件检测）； (2) 对机器学习检测算法的对抗性攻击（例如，对抗性机器学习）。网络安全和机器学习领域也在快速发展，这使得这些各自领域及其交叉领域的教育变得至关重要。通过数据驱动和机器学习方法的大规模在线培训来提高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。该项目团队将通过开发针对职业中期安全专业人员的大规模在线培训课程来满足这一迫切需求，这些专业人员旨在培养将传统和尖端机器学习工具应用于网络安全的技能。该项目将开发机器学习和网络安全交叉点的课程，重点关注机器学习在实际、现实世界安全用例中的应用。此外，该项目将为安全研究人员建立教学基础，以评估和应用各种潜在的基于机器学习的网络安全方法。该项目特别侧重于培训职业中期专业人员，他们接受过网络安全方面的经典培训（从而了解实用概念），但需要在数据驱动方法方面获得更坚实的基础，这些方法已成为大多数人的基础过去十年应用网络安全。项目成果将包括：（1）数据驱动安全在线课程开发，为职业中期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) 形成性研究，以从劳动力中获取所需的技能和用例； (3) 模块化公共工具包和数据集，可在课程中使用，并作为专业人士在实际环境中应用的资源； (4) 基于智能辅导系统，为个别学生量身定制的增强教材。该项目得到安全可信网络空间 (SaTC) 计划特别倡议的支持，旨在促进网络安全领域之间新的、以前未探索过的合作、人工智能和教育。 SaTC 计划与联邦网络安全研究与发展战略计划和国家隐私研究战略相一致，旨在保护和维护网络系统不断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，并被认为是值得的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来提供支持。

项目成果

期刊论文数量（2）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Contextual Active Online Model Selection with Expert Advice

根据专家建议进行上下文主动在线模型选择

DOI：
发表时间：
2022
期刊：
ICML2022 Workshop on Adaptive Experimental Design and Active Learning in the Real World
影响因子：
0
作者：
Liu, Xuefeng;Xia, Fangfang;Stevens, Rick L.;Chen, Yuxin
通讯作者：
Chen, Yuxin

Iterative Machine Teaching for Black-box Markov Learners

黑盒马尔可夫学习者的迭代机器教学