Transcription of Inferring user traits via unsupervised methods
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Inferring user traits via unsupervised methods
Characterizing the ethereum address spaceInferring user traits via unsupervised methodsJames Payette1, Samuel Schwager2, Joseph Murphy31 Department of Computer Science, of MCS, of Physics, AcquisitionData and Feature SetModels and AnalysisResults and DiscussionOngoing InvestigationsReferencesSuccessful,effic ientdataacquisitionwasamajormilestonefor ourproject ,werecursivelyscrapeddatafromthepublical lyavailableblockchain,eventuallyaggregat ingadatasetof250,000uniqueaddresses. QueriedtheetherscanAPIforanaddress ethereumbalanceandalloftheirtransactions ( ).Wetriedtoselectfeaturesthat,whenaggreg ated, :TotalEther,numberoftransactions,transac tionspermonth,averageEthertransaction, ,yetanonymousledgers,or blockchains , ,knownonlybytheiraddresses,wouldhaveenor moussecurityimplications[1].Weexaminethe blockchainofEthereumwiththeobjectiveofcl usteringaddressesintodistinct behaviorgroups example transaction on the ethereum blockchain [2]The Ethereumaddress spaceThemainobjectiveofourquantitativean alysiswastouseclusteringevaluationmetric sandPrincipalComponentAnalysis(PCA)todet ermineaninformedestimatefortheoptimalnum berofclusterswithwhichtoexamineasbehavio rgroups.
feature vector for a single Ethereum address and each column to a single feature. The dataset is normalized to the sample ... "Ethereum: A secure decentralised generalised transaction ledger." Ethereum Project Yellow Paper 151 (2014). [3] Kodinariya, Trupti M., and Prashant R. Makwana. "Review on determining number of Cluster in K-Means
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