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DGA Detection Using Similarity-Preserving Bloom Encodings

Authors:

Lasse Nitz,

Avikarsha MandalAuthors Info & Claims

EICC '23: Proceedings of the 2023 European Interdisciplinary Cybersecurity Conference

Pages 116 - 120

https://doi.org/10.1145/3590777.3590795

Published: 14 June 2023 Publication History

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Abstract

The sanitization of concise data samples can be challenging, as they do not provide a clear distinction between sensitive and non-sensitive parts within individual samples. In this context, traditional sanitization and anonymization measures are not applicable. We consider the detection of algorithmically generated domains through machine learning as an example of such a case, where the benign samples may leak sensitive information. Within this scenario, we evaluate the use of a similarity-preserving Bloom encoding technique to obscure the training samples.

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Cited By

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Akbari Gurabi MHermsen FMandal ADecker S(2024)Towards Privacy-Preserving Machine Learning in Sovereign Data Spaces: Opportunities and ChallengesPrivacy and Identity Management. Sharing in a Digital World10.1007/978-3-031-57978-3_11(158-174)Online publication date: 23-Apr-2024
https://doi.org/10.1007/978-3-031-57978-3_11

Index Terms

DGA Detection Using Similarity-Preserving Bloom Encodings
1. Computing methodologies
  1. Machine learning
2. Security and privacy
  1. Human and societal aspects of security and privacy
    1. Privacy protections
  2. Intrusion/anomaly detection and malware mitigation
    1. Malware and its mitigation

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Published In

EICC '23: Proceedings of the 2023 European Interdisciplinary Cybersecurity Conference

June 2023

205 pages

ISBN:9781450398299

DOI:10.1145/3590777

This work is licensed under a Creative Commons Attribution International 4.0 License.

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Association for Computing Machinery

New York, NY, United States

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Published: 14 June 2023

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EICC 2023

EICC 2023: European Interdisciplinary Cybersecurity Conference

June 14 - 15, 2023

Stavanger, Norway

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Akbari Gurabi MHermsen FMandal ADecker S(2024)Towards Privacy-Preserving Machine Learning in Sovereign Data Spaces: Opportunities and ChallengesPrivacy and Identity Management. Sharing in a Digital World10.1007/978-3-031-57978-3_11(158-174)Online publication date: 23-Apr-2024
https://doi.org/10.1007/978-3-031-57978-3_11

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MET𝔸P: revisiting Privacy-Preserving Data Publishing using secure devices

Protecting sensitive place visits in privacy-preserving trajectory publishing

Privacy-preserving publishing microdata with full functional dependencies

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