Ensemble-based multi-filter feature selection method for DDoS detection in cloud computing

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dc.contributor.authorOsanaiye, Opeyemi
dc.contributor.authorCai, Haibin
dc.contributor.authorChoo, Kim-Kwang Raymond
dc.contributor.authorDehghantanha, Ali
dc.contributor.authorXu, Zheng
dc.contributor.authorDlodlo, Mqhele
dc.date.accessioned2021-10-08T07:04:04Z
dc.date.available2021-10-08T07:04:04Z
dc.date.issued2016
dc.description.abstractAbstract Widespread adoption of cloud computing has increased the attractiveness of such services to cybercriminals. Distributed denial of service (DDoS) attacks targeting the cloud’s bandwidth, services and resources to render the cloud unavailable to both cloud providers, and users are a common form of attacks. In recent times, feature selection has been identified as a pre-processing phase in cloud DDoS attack defence which can potentially increase classification accuracy and reduce computational complexity by identifying important features from the original dataset during supervised learning. In this work, we propose an ensemble-based multi-filter feature selection method that combines the output of four filter methods to achieve an optimum selection. We then perform an extensive experimental evaluation of our proposed method using intrusion detection benchmark dataset, NSL-KDD and decision tree classifier. The findings show that our proposed method can effectively reduce the number of features from 41 to 13 and has a high detection rate and classification accuracy when compared to other classification techniques.
dc.identifier.apacitationOsanaiye, O., Cai, H., Choo, K. R., Dehghantanha, A., Xu, Z., & Dlodlo, M. (2016). Ensemble-based multi-filter feature selection method for DDoS detection in cloud computing. <i>EURASIP Journal on Wireless Communications and Networking</i>, 2016(1), 174 - 177. http://hdl.handle.net/11427/34419en_ZA
dc.identifier.chicagocitationOsanaiye, Opeyemi, Haibin Cai, Kim-Kwang Raymond Choo, Ali Dehghantanha, Zheng Xu, and Mqhele Dlodlo "Ensemble-based multi-filter feature selection method for DDoS detection in cloud computing." <i>EURASIP Journal on Wireless Communications and Networking</i> 2016, 1. (2016): 174 - 177. http://hdl.handle.net/11427/34419en_ZA
dc.identifier.citationOsanaiye, O., Cai, H., Choo, K.R., Dehghantanha, A., Xu, Z. & Dlodlo, M. 2016. Ensemble-based multi-filter feature selection method for DDoS detection in cloud computing. <i>EURASIP Journal on Wireless Communications and Networking.</i> 2016(1):174 - 177. http://hdl.handle.net/11427/34419en_ZA
dc.identifier.issn1687-1472
dc.identifier.issn1687-1499
dc.identifier.ris TY - Journal Article AU - Osanaiye, Opeyemi AU - Cai, Haibin AU - Choo, Kim-Kwang Raymond AU - Dehghantanha, Ali AU - Xu, Zheng AU - Dlodlo, Mqhele AB - Abstract Widespread adoption of cloud computing has increased the attractiveness of such services to cybercriminals. Distributed denial of service (DDoS) attacks targeting the cloud’s bandwidth, services and resources to render the cloud unavailable to both cloud providers, and users are a common form of attacks. In recent times, feature selection has been identified as a pre-processing phase in cloud DDoS attack defence which can potentially increase classification accuracy and reduce computational complexity by identifying important features from the original dataset during supervised learning. In this work, we propose an ensemble-based multi-filter feature selection method that combines the output of four filter methods to achieve an optimum selection. We then perform an extensive experimental evaluation of our proposed method using intrusion detection benchmark dataset, NSL-KDD and decision tree classifier. The findings show that our proposed method can effectively reduce the number of features from 41 to 13 and has a high detection rate and classification accuracy when compared to other classification techniques. DA - 2016 DB - OpenUCT DP - University of Cape Town IS - 1 J1 - EURASIP Journal on Wireless Communications and Networking LK - https://open.uct.ac.za PY - 2016 SM - 1687-1472 SM - 1687-1499 T1 - Ensemble-based multi-filter feature selection method for DDoS detection in cloud computing TI - Ensemble-based multi-filter feature selection method for DDoS detection in cloud computing UR - http://hdl.handle.net/11427/34419 ER - en_ZA
dc.identifier.urihttp://hdl.handle.net/11427/34419
dc.identifier.vancouvercitationOsanaiye O, Cai H, Choo KR, Dehghantanha A, Xu Z, Dlodlo M. Ensemble-based multi-filter feature selection method for DDoS detection in cloud computing. EURASIP Journal on Wireless Communications and Networking. 2016;2016(1):174 - 177. http://hdl.handle.net/11427/34419.en_ZA
dc.language.isoeng
dc.publisher.departmentDepartment of Electrical Engineering
dc.publisher.facultyFaculty of Engineering and the Built Environment
dc.sourceEURASIP Journal on Wireless Communications and Networking
dc.source.journalissue1
dc.source.journalvolume2016
dc.source.pagination174 - 177
dc.source.urihttps://dx.doi.org/10.1186/s13638-016-0623-3
dc.subject.otherEnsemble-based multi-filter feature selection method
dc.subject.otherFilter methods
dc.subject.otherCloud DDoS
dc.subject.otherIntrusion detection system
dc.subject.otherMachining learning
dc.titleEnsemble-based multi-filter feature selection method for DDoS detection in cloud computing
dc.typeJournal Article
uct.type.publicationResearch
uct.type.resourceJournal Article
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