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.