@inproceedings{janez-martino-etal-2024-comparative,
    title = "Comparative Analysis of Natural Language Processing Models for Malware Spam Email Identification",
    author = "J{\'a}{\~n}ez-Martino, Francisco  and
      Fidalgo, Eduardo  and
      Alaiz-Rodr{\'i}guez, Roc{\'i}o  and
      Carofilis, Andr{\'e}s  and
      Mart{\'i}nez-Mendoza, Alicia",
    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-1.7/",
    pages = "59--63",
    abstract = "Spam email is one of the main vectors of cyberattacks containing scams and spreading malware. Spam emails can contain malicious and external links and attachments with hidden malicious code. Hence, cybersecurity experts seek to detect this type of email to provide earlier and more detailed warnings for organizations and users. This work is based on a binary classification system (with and without malware) and evaluates models that have achieved high performance in other natural language applications, such as fastText, BERT, RoBERTa, DistilBERT, XLM-RoBERTa, and Large Language Models such as LLaMA and Mistral. Using the Spam Email Malware Detection (SEMD-600) dataset, we compare these models regarding precision, recall, F1 score, accuracy, and runtime. DistilBERT emerges as the most suitable option, achieving a recall of 0.792 and a runtime of 1.612 ms per email."
}