294 0

Full metadata record

DC FieldValueLanguage
dc.contributor.author임을규-
dc.date.accessioned2018-04-03T01:06:48Z-
dc.date.available2018-04-03T01:06:48Z-
dc.date.issued2014-06-
dc.identifier.citationIn: IET Conference Publications. (IETConference Publications, 2014, 2014(CP639),p.263-268en_US
dc.identifier.urihttp://ieeexplore.ieee.org/document/6912767/-
dc.identifier.urihttp://hdl.handle.net/20.500.11754/55453-
dc.description.abstractEmerging sophisticated malware utilises obfuscation to circumvent detection. This is achieved by using packers to disguise their malicious intent. In this paper a novel malware detection method for detecting packed executable files using entropy analysis is proposed. It utilises a reduced feature set of variables to calculate an entropy score from which classification can be performed. Competitive analysis with state-of-the-art reveals an increase in classification accuracy.en_US
dc.language.isoenen_US
dc.publisherIETen_US
dc.subjectObfuscationen_US
dc.subjectPackingen_US
dc.subjectMalwareen_US
dc.subjectSecurity.en_US
dc.titleFeature set reduction for the detection of packed executablesen_US
dc.typeArticleen_US
dc.identifier.doi10.1049/cp.2014.0696-
dc.relation.page263-268-
dc.contributor.googleauthorBurgess, Colin-
dc.contributor.googleauthorSezer, Sakir-
dc.contributor.googleauthorMcLaughlim, Kieran-
dc.contributor.googleauthorIm, Eul Gyu-
dc.sector.campusS-
dc.sector.daehakCOLLEGE OF ENGINEERING[S]-
dc.sector.departmentDEPARTMENT OF COMPUTER SCIENCE-
dc.identifier.pidimeg-
Appears in Collections:
COLLEGE OF ENGINEERING[S](공과대학) > COMPUTER SCIENCE(컴퓨터소프트웨어학부) > Articles
Files in This Item:
There are no files associated with this item.
Export
RIS (EndNote)
XLS (Excel)
XML


qrcode

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

BROWSE