About: On the Automated Assessment of Open-Source Cyber Threat Intelligence Sources

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An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	On the Automated Assessment of Open-Source Cyber Threat Intelligence Sources
Creator	Mühlhäuser, Max Ruppert, Samuel Tundis, Andrea
Source	PMC
abstract	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.
has issue date	2020-06-15(xsd:dateTime)
bibo:doi	10.1007/978-3-030-50417-5_34
has license	no-cc
sha1sum (hex)	f5d545210bee4bb72f484a1447d73a53be5b2f33
schema:url	https://doi.org/10.1007/978-3-030-50417-5_34
resource representing a document's title	On the Automated Assessment of Open-Source Cyber Threat Intelligence Sources
has PubMed Central identifier	PMC7302822
schema:publication	Computational Science - ICCS 2020
resource representing a document's body	covid:f5d545210bee4bb72f484a1447d73a53be5b2f33#body_text
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