Comprehensive Performance Evaluation of Association Rule Mining Algorithms for Border Gateway Protocol Anomaly Detection

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dc.contributor.authorAltamimi, M.
dc.contributor.authorRamaha, N.
dc.contributor.authorAlmohammedi, A.A.
dc.date.accessioned2024-09-29T16:20:57Z
dc.date.available2024-09-29T16:20:57Z
dc.date.issued2024
dc.departmentKarabük Üniversitesien_US
dc.description4th International Conference on Emerging Smart Technologies and Applications, eSmarTA 2024 -- 6 August 2024 through 7 August 2024 -- Sana'a -- 202077en_US
dc.description.abstractAssociation rule mining is a data mining technique that concentrates on uncovering noteworthy connections or associations within a vast database of items. The process entails identifying frequently occurring sets of items and deriving association rules from those sets. Border Gateway Protocol (BGP) serves as the prevalent gateway protocol facilitating communication between autonomous systems, enabling the exchange of routing and reachability information. The occurrence of anomalous behavior in protocol attributes can be attributed to diverse factors such as insufficient provisioning, malicious attacks, traffic or equipment problems, and errors made by network operators. Based on the association rule, the dataset is examined and partitioned into sub-frequent pattern datasets. Each pattern dataset selects a single anomalous frequent itemset for comparison to determine the itemset with the highest anomaly value. The commonly used rule-based machine learning algorithms for anomaly detection include Frequent Pattern (FP) growth and Apriori algorithms. This study aims to use unsupervised algorithms based on association rules in select features to detect BGP anomalies. Additionally, the performance of these algorithms will be evaluated in terms of support, confidence, and accuracy values. Furthermore, the anomaly detection results were presented using a tailored and efficient tool and framework, enhancing the clarity and providing a more precise and detailed understanding of the performance. © 2024 IEEE.en_US
dc.identifier.doi10.1109/eSmarTA62850.2024.10638999
dc.identifier.isbn979-835035413-3
dc.identifier.scopus2-s2.0-85203670635en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://doi.org/10.1109/eSmarTA62850.2024.10638999
dc.identifier.urihttps://hdl.handle.net/20.500.14619/9449
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartof4th International Conference on Emerging Smart Technologies and Applications, eSmarTA 2024en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectApriori Algorithmen_US
dc.subjectAssociation ruleen_US
dc.subjectBGP Anomalyen_US
dc.subjectDataseten_US
dc.subjectFP Algorithmen_US
dc.subjectWekaen_US
dc.titleComprehensive Performance Evaluation of Association Rule Mining Algorithms for Border Gateway Protocol Anomaly Detectionen_US
dc.typeConference Objecten_US

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