Advanced
Please use this identifier to cite or link to this item: https://digital.lib.ueh.edu.vn/handle/UEH/62254
Full metadata record
DC FieldValueLanguage
dc.contributor.authorPande S.-
dc.contributor.otherKhamparia A.-
dc.contributor.otherGupta D.-
dc.contributor.otherThanh D.N.H.-
dc.date.accessioned2021-09-05T02:41:45Z-
dc.date.available2021-09-05T02:41:45Z-
dc.date.issued2021-
dc.identifier.isbn9789811584695-
dc.identifier.urihttp://digital.lib.ueh.edu.vn/handle/UEH/62254-
dc.description.abstractNumerous attacks are performed on network infrastructures. These include attacks on network availability, confidentiality and integrity. Distributed denial-of-service (DDoS) attack is a persistent attack which affects the availability of the network. Command and Control (C & C) mechanism is used to perform such kind of attack. Various researchers have proposed different methods based on machine learning technique to detect these attacks. In this paper, DDoS attack was performed using ping of death technique and detected using machine learning technique by using WEKA tool. NSL-KDD dataset was used in this experiment. Random forest algorithm was used to perform classification of the normal and attack samples. 99.76% of the samples were correctly classified.en
dc.formatPortable Document Format (PDF)-
dc.language.isoeng-
dc.publisherSpringer Science and Business Media Deutschland GmbH-
dc.relation.ispartofRecent Studies on Computational Intelligence. Studies in Computational Intelligence-
dc.relation.ispartofseriesVol. 921-
dc.rightsSpringer Nature Singapore Pte Ltd.-
dc.subjectDDOSen
dc.subjectMachine learningen
dc.subjectNetwork securityen
dc.subjectNSL-KDDen
dc.subjectPing of deathen
dc.subjectRandom foresten
dc.titleDDOS detection using machine learning techniqueen
dc.typeConference Paperen
dc.identifier.doihttps://doi.org/10.1007/978-981-15-8469-5_5-
dc.format.firstpage59-
dc.format.lastpage68-
item.languageiso639-1en-
item.cerifentitytypePublications-
item.openairetypeConference Paper-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.grantfulltextnone-
item.fulltextOnly abstracts-
Appears in Collections:Conference Papers
Show simple item record

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.