TheCICIDS2017datasetisacomprehensiveandwidely-usedbenchmarkdatasetfornetworkintrusiondetectionresearch.CreatedbytheCanadianInstituteforCybersecurity(CIC),thisdatasetprovidesrealisticnetworktrafficcontainingbothnormalactivitiesandvarioustypesofattacks.Thedatasetcapturesadiverserangeofmodernattacks,includingbrute-forceattacks,denial-of-service(DoS)attacks,distributeddenial-of-service(DDoS)attacks,web-basedattacks,infiltrationattempts,andbotnetactivities.Itwascollectedinacontrolledenvironmenttoensurelabeledandaccuratedataformachinelearningandcybersecurityresearch.KeyfeaturesoftheCICIDS2017datasetinclude:-Real-worldnetworktrafficwithlabeledattackandbenignsamples-Avarietyofattackscenariostotestintrusiondetectionsystems(IDS)-Richmetadataincludingflow-basedfeaturesforanalysis-CompatibilitywithmachinelearninganddeeplearningapproachesResearchersandpractitionersusethisdatasettoevaluatetheperformanceofintrusiondetectionsystems,developnewdetectionalgorithms,andimprovecybersecuritydefenses.Itsrealisticnaturemakesitavaluableresourceforadvancingthefieldofnetworksecurity.
