Collaborative Research: CISE-MSI: RPEP: SaTC: HBCU Artificial Intelligence and Cybersecurity (AI-CyS) Research Partnership

该奖项全部或部分由《2021年美国救援计划法案》（公法117-2）资助。该项目汇集了来自七所历史上的黑人学院和大学（HBCUs）和三个国家研究实验室（nrl）的研究人员，在人工智能（AI）和网络安全的交叉领域发展AI- cys研究伙伴关系。网络安全漏洞正在以规模和速度增长，使人类无法主动应对威胁；因此，开发人工智能和机器学习（ML）技术来支持预测和检测能力是一个重要的研究领域。参与AI-CyS的hbcu要么已经拥有，要么正在快速发展网络安全研究能力，通过增加学生和教师的参与和培训，这些能力将得到进一步发展。为了开发这一潜力，项目团队将利用其现有的研究活动和合作，加深与其他hbcu和nrl的关系。nrl将通过定期远程会议和实地访问nrl提供额外的研究资源和指导，围绕共同感兴趣的项目，并为学生提供实习和教师访问nrl的机会。该团队还将通过以下方式扩大合作伙伴关系的影响：(a)举办年度研究会议，将合作伙伴的研究人员聚集在一起；(b)增加额外的研究项目，HBCU和国家实验室合作伙伴，在现有的合作伙伴关系中具有互补的研究和教育兴趣；(c)开发跨大学课程和指导计划，培养HBCU学生成为未来网络安全领域的研究和劳动力领导者。这些努力将共同推进网络安全研究、合作机构的研究能力，以及hbcu学生的研究和教育机会，这些学生通常是计算机领域代表性不足的群体的成员。该伙伴关系将围绕五个种子研究项目进行组织，以最大限度地提高现有的能力和合作。第一个将使用强化学习来改进当前自主网络地图软件代理的目标选择。第二个涉及分析网络跟踪路由数据，以更好地理解和解决其映射路径能力的限制，以便更好地检测和分类网络异常。第三个重点是开发然后防御针对计算机视觉算法的对抗性攻击，攻击者在对象上添加视觉补丁以欺骗对象检测和分类工具。第四部分将分析用于生成“深度伪造”视频的现有工具和算法，以开发近乎实时检测伪造视频的方法，并将其应用于监控和身份验证任务。第五部分将扩展概率序列模型，以开发物联网设备背景下多层次网络堆栈的威胁检测器。除了这些具体的种子研究项目，项目小组还将分析其活动，为研究能力建设工作制定基于证据的最佳实践。这些努力将包括发展机制以扩大当前的合作和培育资源，在HBCU成员之间分享专业知识，以及举办年度研究会议，让来自现有和潜在合作伙伴HBCU的教师和学生展示他们的网络安全研究，并与其他研究人员和资源建立联系。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。

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).This project brings together researchers from seven Historically Black Colleges and Universities (HBCUs) and three National Research Laboratories (NRLs) to develop the AI-CyS research partnership at the intersection of artificial intelligence (AI) and cybersecurity. Cybersecurity vulnerabilities are growing at a scale and speed that strains human capacity to proactively address threats; developing AI and machine learning (ML) techniques to support prediction and detection capabilities is thus an important area of research. The HBCUs involved in AI-CyS either already have or are rapidly developing cybersecurity research capacity that would be further developed by increasing student and faculty involvement and training. To develop that potential, the project team will leverage its existing research activities and collaborations to deepen relationships with both other HBCUs and with the NRLs. The NRLs will provide additional research resources and mentoring through both regular remote meetings and on-site visits to the NRLs around projects of mutual interest, and opportunities for student internships and faculty visits at the NRLs. The team will also work to expand the impact of the partnership by (a) hosting an annual research conference to bring researchers together across the partners, (b) adding additional research projects, HBCUs and national lab partners with complementary research and educational interests to the existing partnership, and (c) developing cross-university curricula and mentoring programs to train HBCU students to be future research and workforce leaders in cybersecurity. Together, these efforts will advance research in cybersecurity, research capacity at the partner institutions, and research and educational opportunities for students at HBCUs, who are often members of underrepresented groups in computing. The partnership will be organized around five seed research projects chosen to maximize existing capacity and collaborations. The first will use reinforcement learning to improve target selection by current autonomous network mapping software agents. The second involves analyzing network traceroute data to better understand and work around limitations of its ability to map paths in order to better detect and classify network anomalies. The third focuses on developing, then defending against, adversarial attacks on computer vision algorithms in which attackers add visual patches to objects to fool object detection and classification tools. The fourth will analyze existing tools and algorithms for generating “deepfake” videos to develop methods to detect forged video in near real-time, with applications to surveillance and authentication tasks. The fifth will extend probabilistic sequential models to develop threat detectors at multiple levels of the network stack in the context of Internet of Things devices. Beyond these specific seed research projects, the project team will also analyze its activities to develop evidence-based best practices for research capacity-building efforts. These efforts will include developing mechanisms to expand current collaborations and foster resources, sharing expertise between HBCU members, and hosting an annual research conference that allows faculty and students from both current and potential partner HBCUs to showcase their cybersecurity research and create connections to other researchers and resources.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.