January 30, 2021 Journal article Open Access

Manish Tiwari; Dr. Tripti Arjariya

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  <identifier identifierType="URL">https://zenodo.org/record/5833442</identifier>
  <creators>
    <creator>
      <creatorName>Manish Tiwari</creatorName>
      <affiliation>Computer Science from Gargi Institute of Science  &amp; Technology, Bhopal Madhya Pradesh. India</affiliation>
    </creator>
    <creator>
      <creatorName>Dr. Tripti Arjariya</creatorName>
      <affiliation>Professor, Department of Computer Science  Engineering, Rajiv Gandhi Technical University, Bhopal. Madhya Pradesh. India</affiliation>
    </creator>
  </creators>
  <titles>
    <title>A Phishing URL Classification Technique using  Machine Learning Approach</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2021</publicationYear>
  <subjects>
    <subject>Phishing detection, URL classification, association rule mining, rule based classification, apriori algorithm, FP-Tree algorithm.</subject>
    <subject subjectScheme="issn">2278-3075</subject>
    <subject subjectScheme="handle">100.1/ijitee.C83380110321</subject>
  </subjects>
  <contributors>
    <contributor contributorType="Sponsor">
      <contributorName>Blue Eyes Intelligence Engineering and Sciences Publication(BEIESP)</contributorName>
      <affiliation>Publisher</affiliation>
    </contributor>
  </contributors>
  <dates>
    <date dateType="Issued">2021-01-30</date>
  </dates>
  <language>en</language>
  <resourceType resourceTypeGeneral="JournalArticle"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/5833442</alternateIdentifier>
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  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="ISSN" relationType="IsCitedBy" resourceTypeGeneral="JournalArticle">2278-3075</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.35940/ijitee.C8338.0110321</relatedIdentifier>
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  <rightsList>
    <rights rightsURI="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;The phishing attack is one of the very common attacks deployed using the social engineering techniques. The attack tries to capture the victim&amp;rsquo;s personal and sensitive information to trick and can results in terms of financial and social reputation loss. In this presented work the main focus is to investigate the phishing techniques and their detection approaches. In this context first a review on recently contributed URL based phishing attack detection and prevision techniques is prepared. Further based on the suitable techniques a new data mining based model is proposed for implementation. The proposed model first take training on phish tank database URLs and then identify the similar pattern based URLs in two classes legitimate and phishing. First the dataset is preprocessed and the features are computed. The computed features are then transformed in terms of transactional database and association rules are prepared. To generate the association rules the apriori algorithm and FP-Tree algorithm is employed. Based on conducted experiments, the performance the FP-Tree based classification technique much efficient and accurate as compared to apriori algorithm, because the apriori algorithm is much time expensive then the FP-Tree. Finally the future extension of the work is also suggested.&lt;/p&gt;</description>
  </descriptions>
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