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<ArticleSet>
  <Article>
    <Journal>
      <PublisherName>IJIRCSTJournal</PublisherName>
      <JournalTitle>International Journal of Innovative Research in Computer Science and Technology</JournalTitle>
      <PISSN>I</PISSN>
      <EISSN>S</EISSN>
      <Volume-Issue>Volume 12 Issue 2</Volume-Issue>
      <PartNumber/>
      <IssueTopic>Computer Science </IssueTopic>
      <IssueLanguage>English</IssueLanguage>
      <Season>March - April 2024</Season>
      <SpecialIssue>N</SpecialIssue>
      <SupplementaryIssue>N</SupplementaryIssue>
      <IssueOA>Y</IssueOA>
      <PubDate>
        <Year>2024</Year>
        <Month>04</Month>
        <Day>03</Day>
      </PubDate>
      <ArticleType>Computer Sciences</ArticleType>
      <ArticleTitle>A Comparative Approach for Host Based Intrusion Detection Using Naiyve Bayes and KNN Algorithm</ArticleTitle>
      <SubTitle/>
      <ArticleLanguage>English</ArticleLanguage>
      <ArticleOA>Y</ArticleOA>
      <FirstPage>87</FirstPage>
      <LastPage>90</LastPage>
      <AuthorList>
        <Author>
          <FirstName>Pushpendra Chaturvedi </FirstName>          
          <AuthorLanguage>English</AuthorLanguage>
          <Affiliation/>
          <CorrespondingAuthor>Y</CorrespondingAuthor>
          <ORCID/>
             
        </Author>
      </AuthorList>
      <DOI>https://doi.org/10.55524/ijircst.2024.12.2.15</DOI>
      <Abstract>Despite the existence of various types of network intrusion detection system, growth of attacks at host level has increased in the present time. Therefore, there is a huge potential of research in this field and which motivates this research work. This paper analyses the pattern of four classes of attacks used to deploy host-based intrusion. KNN and Na&amp;iuml;ve-Bayes algorithms are employed and compared in this research work to determine the presence of intrusion using standard measures of performance.</Abstract>
      <AbstractLanguage>English</AbstractLanguage>
      <Keywords>Intrusion detection, K-NN, Naïve - Bayes</Keywords>
      <URLs>
        <Abstract>https://ijircst.org/abstract.php?article_id=1242</Abstract>
      </URLs>      
    </Journal>
  </Article>
</ArticleSet>