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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 1</Volume-Issue>
      <PartNumber/>
      <IssueTopic>Computer Science and Engineering</IssueTopic>
      <IssueLanguage>English</IssueLanguage>
      <Season>January - February 2024</Season>
      <SpecialIssue>N</SpecialIssue>
      <SupplementaryIssue>N</SupplementaryIssue>
      <IssueOA>Y</IssueOA>
      <PubDate>
        <Year>2024</Year>
        <Month>02</Month>
        <Day>14</Day>
      </PubDate>
      <ArticleType>Computer Sciences</ArticleType>
      <ArticleTitle>Analysis of Cyber Security Threats Using Machine Learning Techniques</ArticleTitle>
      <SubTitle/>
      <ArticleLanguage>English</ArticleLanguage>
      <ArticleOA>Y</ArticleOA>
      <FirstPage>35</FirstPage>
      <LastPage>39</LastPage>
      <AuthorList>
        <Author>
          <FirstName>Ranjana B Nadagoudar</FirstName>          
          <AuthorLanguage>English</AuthorLanguage>
          <Affiliation/>
          <CorrespondingAuthor>Y</CorrespondingAuthor>
          <ORCID/>
             
        </Author>
      </AuthorList>
      <DOI>https://doi.org/10.55524/ijircst.2024.12.1.7 </DOI>
      <Abstract>Nowadays malware detection is a problem that researchers have tried to solve for so many years by using enormous type of methods. The behaviors of two given malware variants remain similar, although their signatures could also be distinct. The proposed project mainly concentrates on classifying the malware families by considering the malware API sequence or API commands. This type of classification is helpful for the analyst as it helps them to get a better insight into the functioning of the malware.</Abstract>
      <AbstractLanguage>English</AbstractLanguage>
      <Keywords>Malware Detection, Malware Family Detection, KNN, SVM, API Calls Argument</Keywords>
      <URLs>
        <Abstract>https://ijircst.org/abstract.php?article_id=1225</Abstract>
      </URLs>      
    </Journal>
  </Article>
</ArticleSet>