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Be the Phisher -- Understanding Users' Perception of Malicious Domains

Published: 05 October 2020 Publication History

Abstract

Attackers use various domain squatting techniques to convince users that their services are legitimate. Previous work has shown that methods liketyposquatting, where single characters are removed or duplicated, can successfully deceive users.
In this paper, we present a study that evaluates how well participants distinguish malicious from benign domains before and after they learned and applied domain squatting techniques themselves. In a multi-part survey, 288 participants create 2,880 malicious domains based on common domain squatting techniques and rate both domains created by other participants and real-world phishing domains in terms of how convincing they are. Our key results show that participants have problems to identify legitimate domains as benign if they include unusual top-level domains, additional terms, or use subdomains. Moreover, participants rated domains created by other participants higher than real-world phishing domains. Overall, we find that participants are more sceptic of domains, and flag more benign domains as malicious, if they contain domain squatting characteristics after they gained practical experience creating phishing domains themselves. In particular, the number of falsely classified domains that were actually benign increased from 33.7% to 46.6% after our training. Our results show that training users to act as an adversary can help to increase the effectiveness of security trainings. In addition, we recommend that online services do not create domains that make use of common domain squatting techniques, to reduce confusion for users.

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  • (2025)DomainHarvester: Uncovering Trustworthy Domains Beyond Popularity RankingsIEEE Access10.1109/ACCESS.2025.353988213(28167-28188)Online publication date: 2025
  • (2024)Dealing with Bad Apples: Organizational Awareness and Protection for Bit-flip and Typo-Squatting AttacksProceedings of the 19th International Conference on Availability, Reliability and Security10.1145/3664476.3664518(1-11)Online publication date: 30-Jul-2024
  • (2024)Smishing Dataset I: Phishing SMS Dataset from Smishtank.comProceedings of the Fourteenth ACM Conference on Data and Application Security and Privacy10.1145/3626232.3653282(289-294)Online publication date: 19-Jun-2024
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cover image ACM Conferences
ASIA CCS '20: Proceedings of the 15th ACM Asia Conference on Computer and Communications Security
October 2020
957 pages
ISBN:9781450367509
DOI:10.1145/3320269
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Publication History

Published: 05 October 2020

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Author Tags

  1. domains
  2. phishing
  3. user-study

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Cited By

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  • (2025)DomainHarvester: Uncovering Trustworthy Domains Beyond Popularity RankingsIEEE Access10.1109/ACCESS.2025.353988213(28167-28188)Online publication date: 2025
  • (2024)Dealing with Bad Apples: Organizational Awareness and Protection for Bit-flip and Typo-Squatting AttacksProceedings of the 19th International Conference on Availability, Reliability and Security10.1145/3664476.3664518(1-11)Online publication date: 30-Jul-2024
  • (2024)Smishing Dataset I: Phishing SMS Dataset from Smishtank.comProceedings of the Fourteenth ACM Conference on Data and Application Security and Privacy10.1145/3626232.3653282(289-294)Online publication date: 19-Jun-2024
  • (2024)“Hey Google, Remind Me to Be Phished” Exploiting the Notifications of the Google (AI) Assistant on Android for Social Engineering Attacks2024 APWG Symposium on Electronic Crime Research (eCrime)10.1109/eCrime66200.2024.00014(109-122)Online publication date: 24-Sep-2024
  • (2024)ChatPhishDetector: Detecting Phishing Sites Using Large Language ModelsIEEE Access10.1109/ACCESS.2024.348390512(154381-154400)Online publication date: 2024
  • (2023)Unraveling Threat Intelligence Through the Lens of Malicious URL CampaignsProceedings of the 18th Asian Internet Engineering Conference10.1145/3630590.3630600(78-86)Online publication date: 12-Dec-2023
  • (2023)Phishing to improve detectionProceedings of the 2023 European Symposium on Usable Security10.1145/3617072.3617121(334-343)Online publication date: 16-Oct-2023
  • (2023)Commercial Anti-Smishing Tools and Their Comparative Effectiveness Against Modern ThreatsProceedings of the 16th ACM Conference on Security and Privacy in Wireless and Mobile Networks10.1145/3558482.3590173(1-12)Online publication date: 29-May-2023
  • (2022)Phishing with Malicious QR CodesProceedings of the 2022 European Symposium on Usable Security10.1145/3549015.3554172(160-171)Online publication date: 29-Sep-2022
  • (2022)AI-based Sound-Squatting Attack Made Possible2022 IEEE European Symposium on Security and Privacy Workshops (EuroS&PW)10.1109/EuroSPW55150.2022.00053(448-453)Online publication date: Jun-2022
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