Abstract
Outdated technologies pose a significant security threat to websites and hackers often hone in on the oldest pages on a site to discover vulnerabilities. To improve the efficiency of (automated) penetration testers, we invent a machine learning method that predicts the age of a webpage. An HTML-specific tokenizer is trained and used to tokenize HTML bodies, which are then transformed into binary vector encodings (a “bag of tokens”). We train a Multi-Layer Perceptron neural network on such encodings, using historical snapshots of webpages as our training data. Our method achieves a mean absolute error of 1.58 years on validation data held out from training.
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Meinke, K., van der Laan, T.A., Iancu, T., Cakir, C. (2023). A Bag of Tokens Neural Network to Predict Webpage Age. In: Dolev, S., Gudes, E., Paillier, P. (eds) Cyber Security, Cryptology, and Machine Learning. CSCML 2023. Lecture Notes in Computer Science, vol 13914. Springer, Cham. https://doi.org/10.1007/978-3-031-34671-2_12
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