The Use of Statistical Features for Low-Rate Denial of Service Attack Detection

dc.authorscopusid55807299700
dc.authorscopusid24328990900
dc.authorscopusid6603885574
dc.contributor.authorBaykaş, Tunçer
dc.contributor.authorBaykas,T.
dc.contributor.authorAnarim,E.
dc.date.accessioned2024-06-23T21:38:54Z
dc.date.available2024-06-23T21:38:54Z
dc.date.issued2023
dc.departmentKadir Has Universityen_US
dc.department-tempFuladi R., Ericsson Research, Istanbul, Turkey; Baykas T., Kadir Has University, Istanbul, Turkey; Anarim E., Bogazici University, Istanbul, Turkeyen_US
dc.description.abstractLow-rate Denial of Service (LDoS) attacks can significantly reduce the serving capabilities of networks. These attacks involve sending periodic high-intensity pulse data flows, and their harmful effects are like those of traditional DoS attacks. However, LDoS attacks have different attack modes, which make them particularly challenging to detect. The high level of concealment associated with LDoS attacks makes it extremely difficult for traditional DoS detection methods to identify them. This paper explores the potential of using statistical features for LDoS attack detection. The results demonstrate that statistical features can offer promising performance in detecting these types of attacks. Furthermore, through the application of RFE and SHAP analysis, we find that entropy and L-moment-based features play a crucial role in detection. These findings provide important insights into the use of statistical features for network security, which can help to enhance the overall resilience of networks against various types of attacks. © 2023 IEEE.en_US
dc.description.sponsorship1515 Frontier Research and Development Laboratories Support Program, (5169902); Türkiye Bilimsel ve Teknolojik Araştırma Kurumu, TÜBİTAKen_US
dc.identifier.citation2
dc.identifier.doi10.1109/6GNet58894.2023.10317727
dc.identifier.isbn979-835030673-6
dc.identifier.scopus2-s2.0-85179764205
dc.identifier.scopusqualityN/A
dc.identifier.urihttps://doi.org/10.1109/6GNet58894.2023.10317727
dc.identifier.urihttps://hdl.handle.net/20.500.12469/5837
dc.identifier.wosqualityN/A
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartofInternational Conference on 6G Networking, 6GNet 2023 -- 2nd International Conference on 6G Networking, 6GNet 2023 -- 18 October 2023 through 20 October 2023 -- Paris -- 194601en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectexplainable AIen_US
dc.subjectfeature engineeringen_US
dc.subjectLow-rate DDoS attacken_US
dc.subjectmachine learningen_US
dc.subjectRFEen_US
dc.subjectSHAPen_US
dc.titleThe Use of Statistical Features for Low-Rate Denial of Service Attack Detectionen_US
dc.typeConference Objecten_US
dspace.entity.typePublication
relation.isAuthorOfPublicationab26f923-9923-42a2-b21e-2dd862cd92be
relation.isAuthorOfPublication.latestForDiscoveryab26f923-9923-42a2-b21e-2dd862cd92be

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