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  <front>
    <journal-meta>
      <journal-id journal-id-type="publisher-id">48</journal-id>
      <journal-title-group>
        <journal-title>创新教育</journal-title>
        <abbrev-journal-title>innovative education</abbrev-journal-title>
      </journal-title-group>
      <issn>ISSN: 3104-8323 EISSN: 3104-8331</issn>
      <publisher>
        <publisher-name>睿核出版社有限公司</publisher-name>
      </publisher>
    </journal-meta>
    <article-meta>
      <article-id pub-id-type="doi">DOI:10.12429/CXJY2662-4963-202610604</article-id>
      <article-id pub-id-type="publisher-id">15656</article-id>
      <title-group>
        <article-title>基于改进鲸鱼优化算法的网络异常检测</article-title>
      </title-group>
      <contrib-group>
        <contrib contrib-type="author">
          <string-name>许彩芳</string-name>
        </contrib>
      </contrib-group>
      <pub-date pub-type="epub">
        <year>2026</year>
        <month>1</month>
      </pub-date>
      <issue>1</issue>
      <abstract>
        <p>为了解决在高维网络数据下，传统检测方法的特征冗余及参数敏感性问题，通过改进鲸鱼优化算法(WOA)的螺旋更新机制与自适应权重策略，提出混合二进制-连续编码的鲸鱼优化算法特征选择器，构建鲸鱼优化算改进的 XGBoost 算法(WOA-XGBoost)动态加权分类模型。经算法优化设计和数据验证，提升其在 NSL-KDD 数据集上的准确率提升 4%左右，特征维度降低 63%，结合异常检测设计，实现异常流量的风险告警。</p>
      </abstract>
    </article-meta>
  </front>
</article>
