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On the Automated Assessment of Open-Source Cyber Threat Intelligence Sources
Global malware campaigns and large-scale data breaches show how everyday life can be impacted when the defensive measures fail to protect computer systems from cyber threats. Understanding the threat landscape and the adversaries’ attack tactics to perform it represent key factors for enabling an ef...
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/PMC7302822/ http://dx.doi.org/10.1007/978-3-030-50417-5_34 |
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author | Tundis, Andrea Ruppert, Samuel Mühlhäuser, Max |
author_facet | Tundis, Andrea Ruppert, Samuel Mühlhäuser, Max |
author_sort | Tundis, Andrea |
collection | PubMed |
description | Global malware campaigns and large-scale data breaches show how everyday life can be impacted when the defensive measures fail to protect computer systems from cyber threats. Understanding the threat landscape and the adversaries’ attack tactics to perform it represent key factors for enabling an efficient defense against threats over the time. Of particular importance is the acquisition of timely and accurate information from threats intelligence sources available on the web which can provide additional intelligence on emerging threats even before they can be observed as actual attacks. In this paper, an approach to automate the assessment of cyber threat intelligence sources and predict a relevance score for each source is proposed. Specifically, a model based on meta-data and word embedding is defined and experimented by training regression models to predict the relevance score of sources on Twitter. The results evaluation show that the assigned score allows to reduce the waiting time for intelligence verification, on the basis of its relevance, thus improving the time advantage of early threat detection. |
format | Online Article Text |
id | pubmed-7302822 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
record_format | MEDLINE/PubMed |
spelling | pubmed-73028222020-06-19 On the Automated Assessment of Open-Source Cyber Threat Intelligence Sources Tundis, Andrea Ruppert, Samuel Mühlhäuser, Max Computational Science – ICCS 2020 Article Global malware campaigns and large-scale data breaches show how everyday life can be impacted when the defensive measures fail to protect computer systems from cyber threats. Understanding the threat landscape and the adversaries’ attack tactics to perform it represent key factors for enabling an efficient defense against threats over the time. Of particular importance is the acquisition of timely and accurate information from threats intelligence sources available on the web which can provide additional intelligence on emerging threats even before they can be observed as actual attacks. In this paper, an approach to automate the assessment of cyber threat intelligence sources and predict a relevance score for each source is proposed. Specifically, a model based on meta-data and word embedding is defined and experimented by training regression models to predict the relevance score of sources on Twitter. The results evaluation show that the assigned score allows to reduce the waiting time for intelligence verification, on the basis of its relevance, thus improving the time advantage of early threat detection. 2020-06-15 /pmc/articles/PMC7302822/ http://dx.doi.org/10.1007/978-3-030-50417-5_34 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 Tundis, Andrea Ruppert, Samuel Mühlhäuser, Max On the Automated Assessment of Open-Source Cyber Threat Intelligence Sources |
title | On the Automated Assessment of Open-Source Cyber Threat Intelligence Sources |
title_full | On the Automated Assessment of Open-Source Cyber Threat Intelligence Sources |
title_fullStr | On the Automated Assessment of Open-Source Cyber Threat Intelligence Sources |
title_full_unstemmed | On the Automated Assessment of Open-Source Cyber Threat Intelligence Sources |
title_short | On the Automated Assessment of Open-Source Cyber Threat Intelligence Sources |
title_sort | on the automated assessment of open-source cyber threat intelligence sources |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7302822/ http://dx.doi.org/10.1007/978-3-030-50417-5_34 |
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