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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.

Characterizing the Ethereum address space Inferring user traits via unsupervised methods James Payette1, Samuel Schwager2, Joseph Murphy3 1Department of Computer Science, jpayette@stanford.edu 2Department of MCS, sams95@stanford.edu 3Department of Physics, murphyjm@stanford.edu Data Acquisition Data and Feature Set Models and Analysis

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