  We explored leveraging state-of-the-art deep learning, big data, and natural
language processing to enhance the detection of vulnerable web server versions.
Focusing on improving accuracy and specificity over rule-based systems, we
conducted experiments by sending various ambiguous and non-standard HTTP
requests to 4.77 million domains and capturing HTTP response status lines. We
represented these status lines through training a BPE tokenizer and RoBERTa
encoder for unsupervised masked language modeling. We then dimensionality
reduced and concatenated encoded response lines to represent each domain's web
server. A Random Forest and multilayer perceptron (MLP) classified these web
servers, and achieved 0.94 and 0.96 macro F1-score, respectively, on detecting
the five most popular origin web servers. The MLP achieved a weighted F1-score
of 0.55 on classifying 347 major type and minor version pairs. Analysis
indicates that our test cases are meaningful discriminants of web server types.
Our approach demonstrates promise as a powerful and flexible alternative to
rule-based systems.
