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dc.contributor.authorLefoane, Moemedi
dc.contributor.authorGhafir, Ibrahim
dc.contributor.authorKabir, Sohag
dc.contributor.authorAwan, Irfan U.
dc.date.accessioned2022-08-01T13:51:48Z
dc.date.accessioned2022-08-18T10:43:19Z
dc.date.available2022-08-01T13:51:48Z
dc.date.available2022-08-18T10:43:19Z
dc.date.issued2023-01
dc.identifier.citationLefoane M, Ghafir I, Kabir S et al (2022) Unsupervised Learning for Feature Selection: A Proposed Solution for Botnet Detection in 5G Networks. IEEE Transactions on Industrial Informatics. 19(1): 921-929.en_US
dc.identifier.urihttp://hdl.handle.net/10454/19101
dc.descriptionYesen_US
dc.description.abstractThe world has seen exponential growth in deploying Internet of Things (IoT) devices. In recent years, connected IoT devices have surpassed the number of connected non-IoT devices. The number of IoT devices continues to grow and they are becoming a critical component of the national infrastructure. IoT devices' characteristics and inherent limitations make them attractive targets for hackers and cyber criminals. Botnet attack is one of the serious threats on the Internet today. This article proposes pattern-based feature selection methods as part of a machine learning (ML) based botnet detection system. Specifically, two methods are proposed: the first is based on the most dominant pattern feature values and the second is based on Maximal Frequent Itemset (MFI) mining. The proposed feature selection method uses Gini Impurity (GI) and an unsupervised clustering method to select the most influential features automatically. The evaluation results show that the proposed methods have improved the performance of the detection system. The developed system has a True Positive Rate (TPR) of 100% and a False Positive Rate (FPR) of 0% for best performing models. In addition, the proposed methods reduce the computational cost of the system as evidenced by the detection speed of the system.en_US
dc.language.isoenen_US
dc.rights© 2022 IEEE. Reproduced in accordance with the publisher's self-archiving policy. See https://www.ieee.org/publications/rights/index.html for more information.en_US
dc.subjectBotnet attacken_US
dc.subjectInternet of Thingsen_US
dc.subjectNetwork securityen_US
dc.subjectIntrusion detection systemen_US
dc.subjectMachine learningen_US
dc.subjectFeature selectionen_US
dc.titleUnsupervised Learning for Feature Selection: A Proposed Solution for Botnet Detection in 5G Networksen_US
dc.status.refereedYesen_US
dc.date.Accepted2022-07-08
dc.date.application2022-07-19
dc.typeArticleen_US
dc.type.versionAccepted manuscripten_US
dc.identifier.doihttps://doi.org/10.1109/TII.2022.3192044
dc.rights.licenseUnspecifieden_US
dc.date.updated2022-08-01T13:51:50Z
refterms.dateFOA2022-08-18T10:44:55Z
dc.openaccess.statusopenAccessen_US


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