Exploring Cybersecurity Risks in Higher Education Environments with Machine Learning

This paper uses unsupervised machine learning techniques (ML) to explore the risk of cybersecurity breaches or attacks in the higher education sector, including schools, universities, and support organizations. A large sample of higher education institutions (N=848) was analyzed by extracting hypertext data from the websites of educational institutions to identify links that may signal a future cybersecurity breach or attack. ML T-distributed Stochastic Neighbor Embedding (t-SNE) was used to train a model for identifying likely indicators of cybersecurity breach risks. The authors used additional techniques, including radial analysis and correspondence analysis, to visualize the cybersecurity breach signals in the data. In this way, decision-makers should be able to spot anomalies that indicate cybercrime activity or potential cyber-attacks. This paper also exposes the problems of using ML and how to use methods and data triangulation to check reliability and validity.