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URL Inspection Tasks: Helping Users Detect Phishing Links in Emails

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arxiv 2502.20234 v1 pith:NEK2DGXP submitted 2025-02-27 cs.CR

classification cs.CR
keywords tasksphishingdomain-nameuserusersattackscomponentefficacy
verification ladder T0 review T1 audit T2 compute T3 formal
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The most widespread type of phishing attack involves email messages with links pointing to malicious content. Despite user training and the use of detection techniques, these attacks are still highly effective. Recent studies show that it is user inattentiveness, rather than lack of education, that is one of the key factors in successful phishing attacks. To this end, we develop a novel phishing defense mechanism based on URL inspection tasks: small challenges (loosely inspired by CAPTCHAs) that, to be solved, require users to interact with, and understand, the basic URL structure. We implemented and evaluated three tasks that act as ``barriers'' to visiting the website: (1) correct click-selection from a list of URLs, (2) mouse-based highlighting of the domain-name URL component, and (3) re-typing the domain-name. These tasks follow best practices in security interfaces and warning design. We assessed the efficacy of these tasks through an extensive on-line user study with 2,673 participants from three different cultures, native languages, and alphabets. Results show that these tasks significantly decrease the rate of successful phishing attempts, compared to the baseline case. Results also showed the highest efficacy for difficult URLs, such as typo-squats, with which participants struggled the most. This highlights the importance of (1) slowing down users while focusing their attention and (2) helping them understand the URL structure (especially, the domain-name component thereof) and matching it to their intent.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. PiMRef: Detecting and Explaining Ever-evolving Spear Phishing Emails with Knowledge Base Invariants

    cs.CR 2025-07 conditional novelty 6.0 of 10

    PiMRef flags spear phishing by verifying that an email's claimed sender identity matches its actual domain in a knowledge base, and that it contains a call to action.

  2. URL2Graph++: Unified Semantic-Structural-Character Learning for Malicious URL Detection

    cs.CR 2025-09 conditional novelty 5.0 of 10

    URL2Graph++ fuses BERT semantics, character CNN features, and dual word/character co-occurrence graphs to report state-of-the-art malicious URL detection on three public datasets.

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