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相关概念视频

Leaky Scanning02:28

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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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Although black holes were theoretically postulated in the 1920s, they remained outside the domain of observational astronomy until the 1970s.
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The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
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Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different...
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Updated: May 3, 2026

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ProcGCN:基于DGCNN的记忆中检测恶意进程

Heyu Zhang1, Binglong Li1, Shilong Yu1

  • 1College of Cryptographic Engineering, Information Engineering University, Zhengzhou, Henan, China.

PeerJ. Computer science
|August 15, 2024
PubMed
概括
此摘要是机器生成的。

这项研究介绍了ProcGCN,这是一个用于在记忆法医中检测恶意软件的深度学习模型. 它使用函数调用图形,达到98.44%的准确性,并且性能优于静态特征方法.

关键词:
在FCG中,FCG是FCG.全国CNN是什么意思恶意软件检测 恶意软件检测记忆法医学的记忆法医学

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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:

  • 现有的深度学习恶意软件检测方法通常依赖于过程垃圾堆中的静态字节特征,一旦恶意软件在内存中,这些特征可以被改变.
  • 与字节级分析相比,函数调用功能提供了更强大的恶意软件行为表示.

研究的目的:

  • 提出和评估ProcGCN模型,这是一种用于检测存储图像中的恶意进程的新型深度学习方法.
  • 为了利用函数调用图 (FCG) 表示,提高恶意软件检测的准确性和速度.

主要方法:

  • 基于深图卷积神经网络 (DGCNN) 的ProcGCN模型是为记忆法医开发的.
  • 从内存图像中提取了过程倾倒,并生成了它们的函数调用图 (FCG).
  • 使用词袋模型创建了FCG节点的特征向量,并将FCG输入ProcGCN进行分类.

主要成果:

  • 在公开数据集上,ProcGCN模型实现了98.44%的高精度和0.9828的F1得分.
  • 该模型与使用静态特征的现有深度学习方法相比,表现出更高的性能.
  • ProcGCN表现出更快的检测速度,突出其效率.

结论:

  • 函数调用功能与图形表示学习相结合,对于基于内存取证的恶意软件检测是有效的.
  • ProcGCN模型为识别内存图像中的恶意进程提供了一个有希望和高效的解决方案.