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CrowdSource: Automated inference of high level malware functionality from low-level symbols using a crowd trained machine learning model

contributor authorSaxe, Joshua
contributor authorTurner, Rafael
contributor authorBlokhin, Kristina
date accessioned2020-03-12T22:15:04Z
date available2020-03-12T22:15:04Z
date issued2014
identifier other6999417.pdf
identifier urihttps://libsearch.um.ac.ir:443/fum/handle/fum/1076459?show=full
formatgeneral
languageEnglish
publisherIEEE
titleCrowdSource: Automated inference of high level malware functionality from low-level symbols using a crowd trained machine learning model
typeConference Paper
contenttypeMetadata Only
identifier padid8212670
subject keywordsIII-V semiconductors
subject keywordsn aluminium compounds
subject keywordsn dispersion relations
subject keywordsn gallium arsenide
subject keywordsn polaritons
subject keywordsn semiconductor quantum wells
subject keywordsn terahertz wave spectra
subject keywordsn valence bands
subject keywordsn GaAs-Al<
subject keywordssub>
subject keywords0.3<
subject keywords/sub>
subject keywordsGa<
subject keywordssub>
subject keywords0.7<
subject keywords/sub>
subject keywordsAs
subject keywordsn TE polarized THz radiation
subject keywordsn THz intervalence band antipolariton dispersion relations
subject keywordsn active region length
subject keywordsn cavity effect
subject keywordsn dephasing effect
subject keywordsn excited semiconductor quantum well media
subject keywordsn intervalence transitions
identifier doi10.1109/ICTON.2014.6876654
journal titlealicious and Unwanted Software: The Americas (MALWARE), 2014 9th International Conference on
filesize2427257
citations0


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