2018
DOI: 10.1016/j.procs.2018.05.137
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Botnet Detection via mining of network traffic flow

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Cited by 46 publications
(29 citation statements)
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References 7 publications
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“…The result of this study can be said to be just relatively comparable with the work of Mathur et al (2018), since both of the studies were based on network traffic flow and targeting at the botnet attack [33]. However, for the feasibility to apply in the smart factory environment, this study shows to be more feasible because of two reasons.…”
Section: Computer Sciencesupporting
confidence: 79%
See 1 more Smart Citation
“…The result of this study can be said to be just relatively comparable with the work of Mathur et al (2018), since both of the studies were based on network traffic flow and targeting at the botnet attack [33]. However, for the feasibility to apply in the smart factory environment, this study shows to be more feasible because of two reasons.…”
Section: Computer Sciencesupporting
confidence: 79%
“…In the study of Mathur et al, botnet were detected via mining of the network traffic flow with random committee method [33]. The resultant accuracy of the random committee was achieved at 95.3%, which was 1.3% lower than those obtained in this study at 96.66667% for random forest-Weka and 96% for random forest-Rstudio.…”
Section: Computer Sciencecontrasting
confidence: 61%
“…In fact, since botnets utilize normal protocols for C&C communications, the traffic is similar to normal traffic and does not cause network latency [12]. Data mining base detection technique including machine learning, classification, and clustering can be used efficiently to detect botnet C&C traffic [17]. Botminer used data mining methods for botnet detection in C&C traffic.…”
Section: Literature and Previous Studymentioning
confidence: 99%
“…It could be noticed that there were two attack targets by a set of bots on the computer target with an IP address of 147.32.80. 9…”
Section: Fig 7 Threshold Analysis For Ctu Dataset Scenariomentioning
confidence: 99%