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Visualization of big data security — a case study on the KDD99 cup data set

Ruan, Zichan, Miao, Yuantian, Pan, Lei, Patterson, Nicholas and Zhang, Jun 2017, Visualization of big data security — a case study on the KDD99 cup data set, Digital communications and networks, vol. 3, no. 4, pp. 250-259, doi: 10.1016/j.dcan.2017.07.004.

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Title Visualization of big data security — a case study on the KDD99 cup data set
Author(s) Ruan, Zichan
Miao, Yuantian
Pan, LeiORCID iD for Pan, Lei orcid.org/0000-0002-4691-8330
Patterson, NicholasORCID iD for Patterson, Nicholas orcid.org/0000-0003-4565-3614
Zhang, JunORCID iD for Zhang, Jun orcid.org/0000-0002-2189-7801
Journal name Digital communications and networks
Volume number 3
Issue number 4
Start page 250
End page 259
Total pages 10
Publisher Elsevier BV
Place of publication Amsterdam, The Netherlands
Publication date 2017-11
ISSN 2352-8648
Keyword(s) big data visualization
sampling method
MDS
PCA
Summary Cyber security has been thrust into the limelight in the modern technological era because of an array of attacks often bypassing untrained intrusion detection systems (IDSs). Therefore, greater attention has been directed on being able deciphering better methods for identifying attack types to train IDSs more effectively. Keycyber-attack insights exist in big data; however, an efficient approach is required to determine strong attack types to train IDSs to become more effective in key areas. Despite the rising growth in IDS research, there is a lack of studies involving big data visualization, which is key. The KDD99 data set has served as a strong benchmark since 1999; therefore, we utilized this data set in our experiment. In this study, we utilized hash algorithm, a weight table, and sampling method to deal with the inherent problems caused by analyzing big data; volume, variety, and velocity. By utilizing a visualization algorithm, we were able to gain insights into the KDD99 data set with a clear identification of “normal” clusters and described distinct clusters of effective attacks.
Language eng
DOI 10.1016/j.dcan.2017.07.004
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2017, Chongqing University of Posts and Telecommunications
Free to Read? Yes
Use Rights Creative Commons Attribution Non-Commercial No-Derivatives licence
Persistent URL http://hdl.handle.net/10536/DRO/DU:30102410

Document type: Journal Article
Collections: School of Information Technology
Open Access Collection
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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.