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Aninsiderthreatcantakeonmanyformsandfallunderdifferentcategories.Thisincludesmalicious insider, careless/unaware/uneducated/naïve employee, and the third-party contractor. Machine learningtechniqueshavebeenstudiedinpublishedliteratureasapromisingsolutionforsuchthreats. However,theycanbebiasedand/orinaccuratewhentheassociateddatasetishugelyimbalanced. Therefore, this article addresses the insider threat detectionon an extremely imbalanceddataset whichincludesemployingapopularbalancingtechniqueknownasspreadsubsample.Theresults showthatalthoughbalancingthedatasetusingthistechniquedidnotimproveperformancemetrics, itdidimprovethetimetakentobuildthemodelandthetimetakentotestthemodel.Additionally, theauthorsrealisedthatrunningthechosenclassifierswithparametersotherthanthedefaultones hasanimpactonbothbalancedandimbalancedscenarios,buttheimpactissignificantlystronger whenusingtheimbalanceddataset.

Insider Threat Detection Using Supervised Machine Learning Algorithms on an Extremely Imbalanced Dataset