Random Forest of Tensors (RFoT)

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dc.contributor.authorEren, Maksim
dc.contributor.authorNicholas, Charles
dc.contributor.authorMcDonald, Renee
dc.contributor.authorHamer, Chris
dc.date.accessioned2021-08-06T17:35:18Z
dc.date.available2021-08-06T17:35:18Z
dc.date.issued2021-07-14
dc.descriptionPresented at the Malware Technical Exchange Meeting (MTEM), Online, July 13-15, 2021.en_US
dc.description.abstractMachine learning has become an invaluable tool in the fight against malware. Traditional supervised and unsupervised methods are not designed to capture the multidimensional details that are often present in cyber data. In contrast, tensor factorization is a powerful unsupervised data analysis method for extracting the latent patterns that are hidden in a multi-dimensional corpus. In this poster we explore the application of tensors to classification, and we describe a hybrid model that leverages the strength of multi-dimensional analysis combined with clustering. We introduce a novel semisupervised ensemble classifier named Random Forest of Tensors (RFoT) that is based on generating a forest of tensors in parallel, which share the same first dimension, and randomly selecting the remainder of the dimensions and entries of each tensor from the features seten_US
dc.description.urihttps://www.maksimeren.com/abstract/Random_Forest_of_Tensors_RFoT_MTEM.pdfen_US
dc.description.urihttps://www.maksimeren.com/poster/Random_Forest_of_Tensors_RFoT_MTEM.pdf
dc.format.extent2 filesen_US
dc.genrepostersen_US
dc.genreconference papers and proceedingsen_US
dc.identifierdoi:10.13016/m2hgt1-kifu
dc.identifier.urihttp://hdl.handle.net/11603/22328
dc.language.isoen_USen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Computer Science and Electrical Engineering Department Collection
dc.relation.ispartofUMBC Faculty Collection
dc.relation.ispartofUMBC Student Collection
dc.rightsThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.en_US
dc.titleRandom Forest of Tensors (RFoT)en_US
dc.typeTexten_US
dcterms.creatorhttps://orcid.org/0000-0002-4362-0256en_US

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