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Premadoma: An Operational Solution to Prevent Malicious Domain Name Registrations in the .eu TLD

Published: 22 January 2021 Publication History

Abstract

DNS is one of the most essential components of the Internet, mapping domain names to the IP addresses behind almost every online service. Domain names are therefore also a fundamental tool for attackers to quickly locate and relocate their malicious activities on the Internet. In this article, we design and evaluate Premadoma, a solution for DNS registries to predict malicious intent well before a domain name becomes operational. In contrast to blacklists, which only offer protection after some harm has already been done, this system can prevent domain names from being used before they can pose any threats. We advance the state of the art by leveraging recent insights into the ecosystem of malicious domain registrations, focusing explicitly on facilitators employed for bulk registration and similarity patterns in registrant information. We thoroughly evaluate the proposed prediction model’s performance and adaptability on an 11-month testing set and address complex and domain-specific dataset challenges. Moreover, we have successfully deployed Premadoma in the operational environment of the .eu ccTLD registry, resulting in a decline of malicious registrations. Finally, we have identified and quantified three possible evasion patterns and have observed changes in the malicious registration ecosystem since Premadoma has been operationalized.

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Cited By

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  • (2025)Semi-supervised approach for detecting malicious domains in TLDs in their first queryInternational Journal of Information Security10.1007/s10207-025-00996-324:2Online publication date: 18-Feb-2025
  • (2024)SecureReg: Combining NLP and MLP for Enhanced Detection of Malicious Domain Name Registrations2024 International Conference on Electrical, Computer and Energy Technologies (ICECET10.1109/ICECET61485.2024.10698551(1-6)Online publication date: 25-Jul-2024
  • (2024)Comparing Deep Neural Networks and Machine Learning for Detecting Malicious Domain Name Registrations2024 IEEE International Conference on Omni-layer Intelligent Systems (COINS)10.1109/COINS61597.2024.10622643(1-4)Online publication date: 29-Jul-2024
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  1. Premadoma: An Operational Solution to Prevent Malicious Domain Name Registrations in the .eu TLD

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        Published In

        cover image Digital Threats: Research and Practice
        Digital Threats: Research and Practice  Volume 2, Issue 1
        Special Issue on ACSAC'19: Part 2
        March 2021
        160 pages
        EISSN:2576-5337
        DOI:10.1145/3447873
        Issue’s Table of Contents
        This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike International 4.0 License.

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        Association for Computing Machinery

        New York, NY, United States

        Publication History

        Published: 22 January 2021
        Accepted: 01 August 2020
        Received: 01 May 2020
        Published in DTRAP Volume 2, Issue 1

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        Author Tags

        1. Domain name registration
        2. early detection
        3. malicious domains

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        • Research-article
        • Research
        • Refereed

        Funding Sources

        • Flemish Research Programme Cybersecurity
        • Research Fund KU Leuven
        • project commissioned by EURid

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        View all
        • (2025)Semi-supervised approach for detecting malicious domains in TLDs in their first queryInternational Journal of Information Security10.1007/s10207-025-00996-324:2Online publication date: 18-Feb-2025
        • (2024)SecureReg: Combining NLP and MLP for Enhanced Detection of Malicious Domain Name Registrations2024 International Conference on Electrical, Computer and Energy Technologies (ICECET10.1109/ICECET61485.2024.10698551(1-6)Online publication date: 25-Jul-2024
        • (2024)Comparing Deep Neural Networks and Machine Learning for Detecting Malicious Domain Name Registrations2024 IEEE International Conference on Omni-layer Intelligent Systems (COINS)10.1109/COINS61597.2024.10622643(1-4)Online publication date: 29-Jul-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
        • (2021)Detection of Newly Registered Malicious Domains through Passive DNS2021 IEEE International Conference on Big Data (Big Data)10.1109/BigData52589.2021.9671348(3360-3369)Online publication date: 15-Dec-2021

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