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        <datestamp>2025-01-19T12:03:30Z</datestamp>
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          <dc:title>次世代型ウイルス対策ソフトとハイブリッド検出を実装するウイルス対策ソフトに対する回避攻撃</dc:title>
          <dc:title xml:lang="en">Evasion Attacks against Next Generation Anti-Virus Software and Antivirus Software Implementing Hybrid Detection</dc:title>
          <jpcoar:creator>
            <jpcoar:creatorName>新井, 悠</jpcoar:creatorName>
          </jpcoar:creator>
          <jpcoar:creator>
            <jpcoar:creatorName>吉岡, 克成</jpcoar:creatorName>
          </jpcoar:creator>
          <jpcoar:creator>
            <jpcoar:creatorName>松本, 勉</jpcoar:creatorName>
          </jpcoar:creator>
          <jpcoar:creator>
            <jpcoar:creatorName xml:lang="en">Yu, Arai</jpcoar:creatorName>
          </jpcoar:creator>
          <jpcoar:creator>
            <jpcoar:creatorName xml:lang="en">Katsunari, Yoshioka</jpcoar:creatorName>
          </jpcoar:creator>
          <jpcoar:creator>
            <jpcoar:creatorName xml:lang="en">Tsutomu, Matsumoto</jpcoar:creatorName>
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          <jpcoar:subject subjectScheme="Other">[特集:サイバー空間を安全にするコンピュータセキュリティ技術] 次世代型ウイルス対策ソフト，機械学習，AI技術，回避攻撃</jpcoar:subject>
          <datacite:description descriptionType="Other">近年，次世代型ウイルス対策ソフト（Next Generation Anti-Virus: NGAV）という名称で，従来型ウイルス対策ソフトでは不可能であった，未知のマルウェアまでも検出が可能であることを長所としている製品が市場に投入されている．本研究では，証明書を使用して特定の文字列を既知のマルウェアに追加することにより，1,065検体を使用してNGAVに対して最大で約44%の見逃しを発生させた．また機械学習エンジンとシグネチャ検出をハイブリッドで実装している製品に対しても，本手法を用いることで最大で約59%の見逃しを発生させることも確認した．</datacite:description>
          <datacite:description descriptionType="Other">In recent years, Next Generation Anti-Virus (NGAV) products have been introduced to the market, which have the advantage of being able to detect even unknown malware, which is not possible with traditional anti-virus software. In this study, we used 1,065 samples to generate up to approximately 44% misses against NGAV by adding specific strings to known malware using certificates. We also confirmed that our method can generate up to 59% misses against products with hybrid implementations of machine learning engines and signature detection.</datacite:description>
          <dc:publisher xml:lang="ja">情報処理学会</dc:publisher>
          <datacite:date dateType="Issued">2023-09-15</datacite:date>
          <dc:language>jpn</dc:language>
          <dc:type rdf:resource="http://purl.org/coar/resource_type/c_6501">journal article</dc:type>
          <jpcoar:identifier identifierType="DOI">https://doi.org/10.20729/00227599</jpcoar:identifier>
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          <jpcoar:identifierRegistration identifierType="JaLC">10.20729/00227599</jpcoar:identifierRegistration>
          <jpcoar:sourceIdentifier identifierType="ISSN">1882-7764</jpcoar:sourceIdentifier>
          <jpcoar:sourceIdentifier identifierType="NCID">AN00116647</jpcoar:sourceIdentifier>
          <jpcoar:sourceTitle>情報処理学会論文誌</jpcoar:sourceTitle>
          <jpcoar:volume>64</jpcoar:volume>
          <jpcoar:issue>9</jpcoar:issue>
          <jpcoar:pageStart>1287</jpcoar:pageStart>
          <jpcoar:pageEnd>1294</jpcoar:pageEnd>
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            <datacite:date dateType="Available">2025-09-15</datacite:date>
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