Cite this DOI

Longman Publishers (2020). A Survey on Phishing Detection and The Importance of Feature Selection In Data Mining Classification Algorithms. *Journal of Science & Technology*, 11–18. https://doi.org/10.46243/jst.2020.v5.i6.pp11-18

@article{anon2020survey,
  title     = {{A Survey on Phishing Detection and The Importance of Feature Selection In Data Mining Classification Algorithms}},
  journal   = {Journal of Science \& Technology},
  year      = {2020},
  month     = {sep},
  number    = {Volume 5},
  pages     = {11--18},
  publisher = {Longman Publishers},
  issn      = {2456-5660},
  doi       = {10.46243/jst.2020.v5.i6.pp11-18},
  url       = {https://doi.org/10.46243/jst.2020.v5.i6.pp11-18},
  language  = {en},
  abstract  = {: In this era of Internet, the issue of security of information is at its peak. One of the main threats in this cyber world is phishing attacks which is an email or website fraud method that targets the genuine webpage or an email and hacks it without the consent of the end user. There are various techniques which help to classify whether the website or an email is legitimate or fake. The major contributors in the process of detection of these phishing frauds include the classification algorithms, feature selection techniques or dataset preparation methods and the feature extraction that plays an important role in detection as well as in prevention of these attacks. This Survey Paper studies the effect of all these contributors and the approaches that are applied in the study conducted on the recent papers. Some of the classification algorithms that are implemented includes Decision tree, Random Forest , Support Vector Machines, Logistic Regression , Lazy K Star, Naive Bayes and J48 etc.}
}

TY  - JOUR
TI  - A Survey on Phishing Detection and The Importance of Feature Selection In Data Mining Classification Algorithms
JO  - Journal of Science & Technology
PY  - 2020
DA  - 2020/09/22/
IS  - Volume 5
SP  - 11
EP  - 18
PB  - Longman Publishers
SN  - 2456-5660
LA  - en
AB  - : In this era of Internet, the issue of security of information is at its peak. One of the main threats in this cyber world is phishing attacks which is an email or website fraud method that targets the genuine webpage or an email and hacks it without the consent of the end user. There are various techniques which help to classify whether the website or an email is legitimate or fake. The major contributors in the process of detection of these phishing frauds include the classification algorithms, feature selection techniques or dataset preparation methods and the feature extraction that plays an important role in detection as well as in prevention of these attacks. This Survey Paper studies the effect of all these contributors and the approaches that are applied in the study conducted on the recent papers. Some of the classification algorithms that are implemented includes Decision tree, Random Forest , Support Vector Machines, Logistic Regression , Lazy K Star, Naive Bayes and J48 etc.
DO  - 10.46243/jst.2020.v5.i6.pp11-18
UR  - https://doi.org/10.46243/jst.2020.v5.i6.pp11-18
ER  -

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    "type": "article-journal",
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    "DOI": "10.46243/jst.2020.v5.i6.pp11-18",
    "URL": "https://doi.org/10.46243/jst.2020.v5.i6.pp11-18",
    "title": "A Survey on Phishing Detection and The Importance of Feature Selection In Data Mining Classification Algorithms",
    "source": "Smart Scholars DOI Registry",
    "container-title": "Journal of Science & Technology",
    "issued": {
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    "issue": "Volume 5",
    "page": "11-18",
    "publisher": "Longman Publishers",
    "language": "en",
    "abstract": ": In this era of Internet, the issue of security of information is at its peak. One of the main threats in this cyber world is phishing attacks which is an email or website fraud method that targets the genuine webpage or an email and hacks it without the consent of the end user. There are various techniques which help to classify whether the website or an email is legitimate or fake. The major contributors in the process of detection of these phishing frauds include the classification algorithms, feature selection techniques or dataset preparation methods and the feature extraction that plays an important role in detection as well as in prevention of these attacks. This Survey Paper studies the effect of all these contributors and the approaches that are applied in the study conducted on the recent papers. Some of the classification algorithms that are implemented includes Decision tree, Random Forest , Support Vector Machines, Logistic Regression , Lazy K Star, Naive Bayes and J48 etc.",
    "ISSN": "2456-5660"
}

From the record as registered (version 2) — the record and its history. Programs: https://registry.smartscholars.in/api.php?action=cite&doi=10.46243%2Fjst.2020.v5.i6.pp11-18 gives all four in one JSON answer.