Vertical Search Engine and Graph Homomorphism for Enhancing the Cybersecurity Workforce

用于增强网络安全劳动力的垂直搜索引擎和图同态

• 批准号: 1934782
• 负责人: Dongwon Lee
• 金额: $ 30万
• 依托单位: Pennsylvania State Univ University Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-07-01 至 2024-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1934782&HistoricalAwards=false
• 关键词: Vertical; Search; Engine; Graph; Homomorphism

When there is a new or rapidly changing labor market, there is often an information gap between job seekers on documenting their competencies and employers in evaluating credentials. This is particular a challenge in the rapidly expanding cybersecurity workforce. This project will create practical tools to bridge the gap between employers and individuals interested in cybersecurity careers, using the SFS program as a test bed. The project will also support a Community of Practice of cybersecurity educators to encourage the adoption of these innovative tools. This exploratory project may face challenges with collecting and integrating job posting data from many sites and with creating data structures from unstructured texts. However, if successful, this project could have a significant impact by helping cybersecurity career seekers. It may also serve as a pilot to develop similar tools for other labor markets. This project will develop two important and practical tools to support future and current cybersecurity workforce: (1) a vertical search engine, called Bruce, a clearinghouse for finding comprehensive cybersecurity learning resources; and (2) a matchmaking tool between cybersecurity job postings and job seekers' skills and competencies. The project will conduct research to create an accurate, scalable, fine-grained Named Entity Recognition (NER) method that can extract various entities from cybersecurity learning resources, including course title, instructor name, textbook, and exam information. In addition, the proposed research will design a mapping algorithm between posted jobs and job seekers with varying structures and vocabulary beyond a simple syntactic/semantic matching. Cybersecurity job postings will be mapped to the most relevant functions and categories in the NIST NICE Cybersecurity Framework, a modern taxonomy of cybersecurity related activities, roles, skills, and profiles. The project's research approach is to use techniques in graph homomorphism from theoretical computer science and graph embedding from machine learning fields.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.

当有一个新的或快速变化的劳动力市场时，求职者之间通常在记录其能力和雇主评估证书方面的信息差距。 在快速扩大的网络安全劳动力中，这特别是一个挑战。 该项目将创建实用的工具，以使用SFS计划作为测试床来弥合雇主和对网络安全职业感兴趣的个人之间的差距。 该项目还将支持网络安全教育者的实践社区，以鼓励采用这些创新工具。该探索性项目可能会面临挑战，通过收集和集成从许多站点的数据以及从非结构化文本创建数据结构的数据。 但是，如果成功的话，该项目可能会通过帮助网络安全职业寻求者产生重大影响。 它也可以作为开发其他劳动力市场类似工具的试点。该项目将开发两种重要且实用的工具来支持未来和当前的网络安全劳动力：（1）垂直搜索引擎，称为Bruce，这是一个销售机构，是找到全面的网络安全学习资源的交换所； （2）网络安全职位发布与求职者的技能和能力之间的对接工具。该项目将进行研究，以创建一种准确，可扩展的，细粒的命名实体识别（NER）方法，该方法可以从网络安全学习资源中提取各种实体，包括课程标题，讲师姓名，教科书和考试信息。此外，拟议的研究将在张贴的作业和求职者之间设计一种映射算法，这些算法与简单的句法/语义匹配之外具有不同结构和词汇的求职者。网络安全职位发布将映射到NIST NICE网络安全框架中最相关的功能和类别，这是网络安全相关活动，角色，技能和配置文件的现代分类学。该项目的研究方法是从理论计算机科学和机器学习领域的图形嵌入图中使用技术。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子和更广泛影响的评估评估的评估来支持的。