[
  {
    "id": "cybersecurity-risks-higher-education-machine-learning",
    "type": "paper-conference",
    "title": "Exploring Cybersecurity Risks in Higher Education Environments with Machine Learning",
    "author": [
      {
        "family": "Strang",
        "given": "Kenneth David"
      },
      {
        "family": "Vajjhala",
        "given": "Narasimha Rao"
      }
    ],
    "container-title": "2024 4th International Conference on Pervasive Computing and Social Networking (ICPCSN)",
    "issued": {
      "date-parts": [
        [
          2024,
          5
        ]
      ]
    },
    "publisher": "IEEE",
    "URL": "https://www.narasimharao.net/research/cybersecurity-risks-higher-education-machine-learning/",
    "abstract": "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.",
    "keyword": "unsupervised machine learning, cybersecurity breaches, higher education sector, hypertext data analysis, t-distributed stochastic neighbor embedding, pervasive computing, cyber-attack indicators, radial analysis, correspondence analysis, anomaly detection, data triangulation, cybersecurity, higher education, universities, machine learning, unsupervised learning, t-SNE, cyber risk assessment, website security, cybercrime, Python, United States",
    "language": "en"
  }
]