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One of the most prevalent types of cybercrime is phishing, which involves tricking online users into giving over pretending to be a reliable website in order to obtain their private or confidential information.To identify phishing websites, several machine learning-based techniques have been suggested. These techniques utilise characteristics that were extracted from web page samples.But relatively few studies have given effective feature selection for phishing attack detection any serious consideration. In this study, researchers investigate the level of widespread agreement over the specific elements required for phishing detection. They choose the best features from three benchmarked data sets using the Fuzzy Rough Set (FRS) theory. The selected features are input into three popular classifiers for phishing detection. To evaluate the FRS feature selection in developing a descriptive phishing detection, the classifiers are trained using an independent out-of-sample data set made up of 14,000 website samples.
Keywords:
Fuzzy Rough Set, Feature Selection, and Phishing Detection.
Cite Article:
"Phishing Detection Using Fuzzy Rough Set Technique", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.8, Issue 7, page no.991 - 996, July-2023, Available :http://www.ijrti.org/papers/IJRTI2307143.pdf
Downloads:
000205143
ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator