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During most eukaryotic translation processes, the small 40S ribosome subunit scans an mRNA from its 5' end until it encounters the first start AUG codon. The large 60S ribosomal subunit then joins the smaller one to initiate protein synthesis. The location of the translation initiation is largely determined by the nucleotides near the start codon as there may be multiple translation initiation sites present on the mRNA.  Marilyn Kozak discovered that the sequence RCCAUGG (where R...
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使用字节流进行文件级恶意软件检测.

Young-Seob Jeong1, Medard Edmund Mswahili1, Ah Reum Kang2

  • 1Department of Computer Engineering, Chungbuk National University, Cheongju, 28644, South Korea.

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概括
此摘要是机器生成的。

本研究引入了一种用于检测非可执行恶意软件的新方法,通过将流级深度学习结果汇总到文件级检测中,提高了更安全的文档分析的准确性.

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科学领域:

  • 计算机科学 计算机科学
  • 网络安全 网络安全
  • 机器学习 机器学习

背景情况:

  • 互联网拥有越来越多的文档,增加了恶意软件感染的风险.
  • 非可执行恶意软件构成重大威胁,因为用户经常低估其危险性.
  • 深度学习模型显示出分析字节流在非可执行恶意软件检测中的前景.

研究的目的:

  • 解决深度学习模型中流级恶意软件检测的局限性.
  • 提出和验证一种用于检测非可执行文件的文件级恶意软件的新方法.
  • 提高文件中的恶意软件检测的准确性和有效性.

主要方法:

  • 开发一种新的聚合技术,将流层级深度学习输出结合起来.
  • 应用拟议的方法来分析非可执行文件的字节流.
  • 使用注释数据集进行实验验证和性能评估.

主要成果:

  • 拟议的方法有效地汇总了流级结果,用于文件级恶意软件检测.
  • 实验结果表明,F1得分的性能增长为3.37至5.89%.
  • 该方法提高了检测非可执行文档中的恶意软件的能力.

结论:

  • 开发的聚合方法显著提高了文件级恶意软件检测准确度.
  • 这种技术为识别非可执行文件中的威胁提供了更强大的解决方案.
  • 这些发现有助于加强用于文档分析的网络安全措施.