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arXiv Machine Learning By Ralph Elsaghbini, Omran Berjawi, Walid Fahs, Rida Khatoun

Behavioral Fingerprinting and Navigation Prediction in Web Browsing

Read the original on arXiv Machine Learning →

The study examines two behavioral inference tasks—session-level user identification and next-domain prediction—using large-scale anonymous web browsing traces. Classical and neural models are applied to user identification, while graph-based methods combined with Large Language Models (LLMs) are used for next-domain prediction. Results show that short browsing sessions are highly identifiable and future navigation is highly predictable, with LLM-derived semantic features offering only marginal improvements over structural and sequential models.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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