2019 IEEE 21st International Conference on High Performance Computing and Communications; IEEE 17th International Conference On 2019
DOI: 10.1109/hpcc/smartcity/dss.2019.00079
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Detecting Malicious Domains Using Modified SVM Model

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Cited by 5 publications
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“…Across many instances, the study found that utilizing lexical features with the other features can increase the detection efficiency of fraudulent websites using Random Forest (RF) with the precision of 0.98. Furthermore, Zhu and Zou et al [15] discovered that, as the detection procedure progresses, the detection performance of a standard Support Vector Machine (SVM) model decreases; however, a revised SVM technique (F-SVM) proposed by the study is effective in maintaining a significant precision rate throughout the detection phase. Due to its high performance, it is well-suited for online detection.…”
Section: Related Studiesmentioning
confidence: 99%
“…Across many instances, the study found that utilizing lexical features with the other features can increase the detection efficiency of fraudulent websites using Random Forest (RF) with the precision of 0.98. Furthermore, Zhu and Zou et al [15] discovered that, as the detection procedure progresses, the detection performance of a standard Support Vector Machine (SVM) model decreases; however, a revised SVM technique (F-SVM) proposed by the study is effective in maintaining a significant precision rate throughout the detection phase. Due to its high performance, it is well-suited for online detection.…”
Section: Related Studiesmentioning
confidence: 99%