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相关概念视频

MALDI-TOF Mass Spectrometry01:19

MALDI-TOF Mass Spectrometry

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Mass spectrometry is a powerful characterization technique that can identify and separate a wide variety of compounds ranging from chemical to biological entities, based on their mass-to-charge ratio (m/z). The instruments that allow this detection, known as mass spectrometers, have three components: an ion source, a mass analyzer, and a detector. These spectrometers differ based on the nature of their ion source and analyzers.
Matrix-assisted laser desorption ionization (MALDI) is a commonly...
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Tagging and Fusion Proteins01:24

Tagging and Fusion Proteins

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Proteins are involved in several cellular processes and biochemical reactions. Analyzing a specific protein of interest requires it to be isolated from the other proteins in the cell. This is achieved by overexpressing the specific gene in a suitable host to produce large quantities of the target protein. A tag or label is recombined with the gene to produce a fusion protein containing the target protein and the tag. The tags on these fusion proteins can then be used for easy detection and...
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Peptide Identification Using Tandem Mass Spectrometry01:33

Peptide Identification Using Tandem Mass Spectrometry

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Tandem mass spectrometry, also known as MS/MS or MS2, is an analytical technique that employs two mass analyzers. Essentially it is a series of mass spectrometers that helps isolate a particular biomolecule and then helps study its chemical properties.
This technique helps gather information regarding the protein from which the peptide was obtained and to study the peptides’ amino acid sequence. Identifying peptides from a complex mixture is an important component of the growing field of...
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Viral Recombination00:57

Viral Recombination

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Cells are sometimes infected by more than one virus at once. When two viruses disassemble to expose their genomes for replication in the same cell, similar regions of their genomes can pair together and exchange sequences in a process called recombination. Alternatively, viruses with segmented genomes can swap segments in a process called reassortment.
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相关实验视频

Updated: Jun 22, 2025

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
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Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

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基于多功能融合的APT恶意软件的归因分类方法.

Jian Zhang1, Shengquan Liu1, Zhihua Liu1

  • 1School of Computer Science and Technology, Xinjiang University, Xinjiang Uygur Autonomous Region, Urumqi, People's Republic of China.

PloS one
|June 27, 2024
PubMed
概括

本研究引入了一种新的多功能深度学习模型,用于高级持久威胁 (APT) 恶意软件归因. 通过整合不同的数据,该模型显著提高了分类准确性,而不是单一特征的方法.

科学领域:

  • 网络安全 网络安全
  • 恶意软件分析 恶意软件分析
  • 机器学习 机器学习

背景情况:

  • 由于互联网的发展,高级持久威胁 (APT) 恶意软件的归因分类至关重要.
  • 现有的方法忽略DLL链接库,隐藏文件地址,并与本地/全球事件行为相关性作斗争.
  • 像二进制结构或操作代码这样的单个特征容易被模糊,并且无法在APT组内捕获重复使用的行为.

研究的目的:

  • 开发一个强大的APT恶意软件归属分类方法.
  • 解决现有方法在特征提取和相关性分析方面的局限性.
  • 为了提高恶意软件归因的准确性和可靠性.

主要方法:

  • 使用API指令和操作构建了一个事件行为图,通过图形神经网络 (GNN) 捕获主机执行痕迹.
  • 使用图像卷积神经网络 (ImageCNTM) 来捕获opcode图像中的本地空间相关性和长期依赖性.
  • 通过连接和融合单词频率和行为特征,提出了一种多功能,多输入的深度学习模型.

主要成果:

  • 单一特征分类器的归因分类率分别为89.24%和91.91%.
  • 与单一特征方法相比,多特征融合模型表现出优越的分类性能.
  • 拟议的模型有效地捕捉了事件行为中的本地和全球相关性.

更多相关视频

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Oncogenic Gene Fusion Detection Using Anchored Multiplex Polymerase Chain Reaction Followed by Next Generation Sequencing

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Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
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Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

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相关实验视频

Last Updated: Jun 22, 2025

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
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Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

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Oncogenic Gene Fusion Detection Using Anchored Multiplex Polymerase Chain Reaction Followed by Next Generation Sequencing
09:49

Oncogenic Gene Fusion Detection Using Anchored Multiplex Polymerase Chain Reaction Followed by Next Generation Sequencing

Published on: July 5, 2019

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Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
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Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

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结论:

  • 多功能融合显著提高APT恶意软件归属分类准确性.
  • 开发的深度学习模型通过整合各种行为和结构特征提供了更全面的方法.
  • 这种方法提供了一种更具弹性的解决方案,可以抵御影响单特征分类器的模糊化技术.