N-grams-based file signatures for malware detection

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dc.contributor.authorSantos Grueiro, Igor
dc.contributor.authorPeña Landaburu, Yoseba Koldobika
dc.contributor.authorDevesa Esteban, Jaime
dc.contributor.authorGarcía Bringas, Pablo
dc.date.accessioned2026-10-14T09:50:43Z
dc.date.available2026-10-14T09:50:43Z
dc.date.issued2009
dc.date.updated2026-10-14T09:50:43Z
dc.descriptionPonencia presentada en la 11ª International Conference on Enterprise Information Systems (ICEIS 2009), celebrado/a en Milán entre el 6 y el 10 de mayo de 2009.es
dc.description.abstractMalware is any malicious code that has the potential to harm any computer or network. The amount of malware is increasing faster every year and poses a serious security threat. Thus, malware detection is a critical topic in computer security. Currently, signature-based detection is the most extended method for detecting malware. Although this method is still used on most popular commercial computer antivirus software, it can only achieve detection once the virus has already caused damage and it is registered. Therefore, it fails to detect new malware. Applying a methodology proven successful in similar problem-domains, we propose the use of n-grams (every substring of a larger string, of a fixed lenght n) as file signatures in order to detect unknown malware whilst keeping low false positive ratio. We show that n-grams signatures provide an effective way to detect unknown malware.en
dc.identifier.citationSantos, I., Penya, Y. K., Devesa, J., & Bringas, P. G. (2009). N-grams-based file signatures for malware detection. ICEIS 2009 - 11th International Conference on Enterprise Information Systems, Proceedings, AIDSS, 317-320. https://doi.org/10.5220/0001863603170320
dc.identifier.doi10.5220/0001863603170320
dc.identifier.isbn9789898111845
dc.identifier.urihttps://hdl.handle.net/20.500.14454/6790
dc.language.isoeng
dc.publisherINSTICC Press
dc.rights© SciTePress
dc.subject.otherSecurity
dc.subject.otherComputer viruses
dc.subject.otherData-mining
dc.subject.otherMalware detection
dc.subject.otherMachine learning
dc.titleN-grams-based file signatures for malware detectionen
dc.typeconference paper
dcterms.accessRightsopen access
oaire.citation.endPage320
oaire.citation.startPage317
oaire.citation.titleICEIS 2009 - 11th International Conference on Enterprise Information Systems, Proceedings
oaire.citation.volumeAIDSS
oaire.licenseConditionhttps://creativecommons.org/licenses/by-nc-nd/4.0/
oaire.versionVoR
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