?url_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rft.relation=https%3A%2F%2Feprints.uet.vnu.edu.vn%2Feprints%2Fid%2Feprint%2F4266%2F&rft.title=Detection+of+Distributed+Denial+of+Service+Attacks+using+Automatic+Feature+Selection+with+Enhancement+for+Imbalance+Dataset&rft.creator=Can%2C+Duy+Cat&rft.subject=Information+Technology+(IT)&rft.description=Abstract%3A+With++the++development++of++technology%2C++the++highly++accessible+internet+service+is+the+biggest+demand+for+most+people.+Online+network%2C+however%2C+has+been+suffering+from+malicious+attempts+to+disrupt+essential+web+technologies%2C+resulting+in+service+failures.+In+this+work%2C+we+introduced+a+model+to+detect+and+classify+Distributed+Denial+of+Service+attacks+based+on++neural++networks++that++take++advantage++of++a++proposed++automatic++feature+selection+component.+The+experimental+results+on+CIC-DDoS+2019dataset+have+demonstrated+that+our+proposed+model+outperformed+other+machine+learning-based+model+by+large+margin.+We+also+investigated+the+effectiveness+of+weighted+loss+and+hinge+loss+on+handling+the+class+imbalance+problem.&rft.publisher=ACIIDS+2021&rft.date=2020&rft.type=Technical+Report&rft.type=NonPeerReviewed&rft.format=application%2Fpdf&rft.language=en&rft.identifier=https%3A%2F%2Feprints.uet.vnu.edu.vn%2Feprints%2Fid%2Feprint%2F4266%2F1%2Ftechnical_report_ACIIDS_2021.pdf&rft.identifier=++Can%2C+Duy+Cat++(2020)+Detection+of+Distributed+Denial+of+Service+Attacks+using+Automatic+Feature+Selection+with+Enhancement+for+Imbalance+Dataset.++Technical+Report.+ACIIDS+2021.++++(Submitted)++