Please use this identifier to cite or link to this item: https://repositorio.unb.br/handle/10482/21543
Files in This Item:
File Description SizeFormat
DC FieldValueLanguage
dc.contributor.authorSantos, Leandro Silva dos-
dc.date.accessioned2016-10-11T20:44:07Z-
dc.date.available2016-10-11T20:44:07Z-
dc.date.issued2016-10-11-
dc.date.submitted2016-06-30-
dc.identifier.citationSANTOS, Leandro Silva dos. Maldetect: uma metodologia automatizável de detecção de malwares desconhecidos. 2016. xv, 76 f., il. Dissertação (Mestrado em Engenharia Elétrica)—Universidade de Brasília, Brasília, 2016.en
dc.identifier.urihttp://repositorio.unb.br/handle/10482/21543-
dc.description.abstractThe scenario of cyber attacks, following the modernization of detection and removal tools is becoming increasingly complex to be detected and mitigated. Thus the traditional tools of detection and removal of threats are becoming less efficient, especially by those that use a signature-based detection approach. This paper proposes an automatable method of detecting unknown malware, ie those that were not detected by traditional tools. The methodology presented in this work, called here by Maldetect, collects and correlates typical behavioral characteristics of malicious code, which are present in the volatile memory dump , in order to identify artifacts that most perform typical activities of malware. Moreover, it was built a tool using the languages of PHP and Python, called Maldetect Tool, which automates the proposed methodology. This tool analyzed the volatile memory dumps infected with malicious code and generated a report containing the artifacts held more typical activities of malware.en
dc.language.isoPortuguêsen
dc.rightsAcesso Abertoen
dc.titleMaldetect : uma metodologia automatizável de detecção de malwares desconhecidosen
dc.typeDissertaçãoen
dc.subject.keywordAtaques cibernéticosen
dc.subject.keywordMalwares - detecçãoen