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Cyber Attribution from Topological Patterns
We developed a crawler to collect live malware distribution network data from publicly available sources including Google Safe Browser and VirusTotal. We then generated a dynamic graph with our visualization tool and performed malware attribution analysis. We found: 1) malware distribution networks...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7304784/ http://dx.doi.org/10.1007/978-3-030-50433-5_5 |
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author | Cai, Yang Andre Morales, Jose Sun, Guoming |
author_facet | Cai, Yang Andre Morales, Jose Sun, Guoming |
author_sort | Cai, Yang |
collection | PubMed |
description | We developed a crawler to collect live malware distribution network data from publicly available sources including Google Safe Browser and VirusTotal. We then generated a dynamic graph with our visualization tool and performed malware attribution analysis. We found: 1) malware distribution networks form clusters rather than a single network; 2) those cluster sizes follow the Power Law; 3) there is a correlation between cluster size and the number of malware species in the cluster; 4) there is a correlation between the number of malware species and cyber events; and finally, 5) infrastructure components such as bridges, hubs, and persistent links play significant roles in malware distribution dynamics. |
format | Online Article Text |
id | pubmed-7304784 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
record_format | MEDLINE/PubMed |
spelling | pubmed-73047842020-06-22 Cyber Attribution from Topological Patterns Cai, Yang Andre Morales, Jose Sun, Guoming Computational Science – ICCS 2020 Article We developed a crawler to collect live malware distribution network data from publicly available sources including Google Safe Browser and VirusTotal. We then generated a dynamic graph with our visualization tool and performed malware attribution analysis. We found: 1) malware distribution networks form clusters rather than a single network; 2) those cluster sizes follow the Power Law; 3) there is a correlation between cluster size and the number of malware species in the cluster; 4) there is a correlation between the number of malware species and cyber events; and finally, 5) infrastructure components such as bridges, hubs, and persistent links play significant roles in malware distribution dynamics. 2020-05-25 /pmc/articles/PMC7304784/ http://dx.doi.org/10.1007/978-3-030-50433-5_5 Text en © Springer Nature Switzerland AG 2020 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Article Cai, Yang Andre Morales, Jose Sun, Guoming Cyber Attribution from Topological Patterns |
title | Cyber Attribution from Topological Patterns |
title_full | Cyber Attribution from Topological Patterns |
title_fullStr | Cyber Attribution from Topological Patterns |
title_full_unstemmed | Cyber Attribution from Topological Patterns |
title_short | Cyber Attribution from Topological Patterns |
title_sort | cyber attribution from topological patterns |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7304784/ http://dx.doi.org/10.1007/978-3-030-50433-5_5 |
work_keys_str_mv | AT caiyang cyberattributionfromtopologicalpatterns AT andremoralesjose cyberattributionfromtopologicalpatterns AT sunguoming cyberattributionfromtopologicalpatterns |