@inproceedings{schaberreiter-etal-2024-human,
    title = "Human-in-the-loop Anomaly Detection and Contextual Intelligence for Enhancing Cybersecurity Management",
    author = "Schaberreiter, Thomas  and
      Andriessen, Jerry  and
      Cappiello, Cinzia  and
      Papanikolaou, Alex  and
      Pardijs, Mirjam",
    editor = "Mitkov, Ruslan  and
      Ezzini, Saad  and
      Ranasinghe, Tharindu  and
      Ezeani, Ignatius  and
      Khallaf, Nouran  and
      Acarturk, Cengiz  and
      Bradbury, Matthew  and
      El-Haj, Mo  and
      Rayson, Paul",
    booktitle = "Proceedings of the First International Conference on Natural Language Processing and Artificial Intelligence for Cyber Security",
    month = jul,
    year = "2024",
    address = "Lancaster, UK",
    publisher = "International Conference on Natural Language Processing and Artificial Intelligence for Cyber Security",
    url = "https://preview.aclanthology.org/fix-sig-urls/2024.nlpaics-1.15/",
    pages = "127--136",
    abstract = "Cybersecurity management is a sociotechnical problem comprising organisational knowledge management of humans and technology. Focusing on risk and incident management, we present our approach for enhancing cybersecurity awareness in organisations and ecosystems. By augmenting our cybersecurity awareness platform with human-in-the-loop anomaly detection and machine learning, we are able to handle the dynamics of organisational human activity, as well as the continuous developments in the cybersecurity domain. We illustrate the potential impact of our approach with a realistic example in the healthcare context"
}