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相关概念视频

Receptor-mediated Endocytosis01:38

Receptor-mediated Endocytosis

Overview
Smooth Endoplasmic Reticulum01:21

Smooth Endoplasmic Reticulum

Smooth endoplasmic reticulum or smooth ER is a sub-organelle with specialized functions in animal cells and plant cells. It is often associated with the tubule morphology of the endoplasmic reticulum.
The ER provides optimal conditions for synthesizing steroid hormones and lipids, such as phospholipids and triglycerides. Traditionally, lipid metabolism was considered to be a smooth ER function. However, there is no direct evidence to prove that rough ER is completely excluded from lipid...
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Liver Regeneration

The liver is an important organ in vertebrates that plays an essential role in metabolism. It is also responsible for storing and redistributing nutrients such as carbohydrates, fats, and vitamins in the body. Additionally, the liver releases bile salts which are critical for digesting food and eliminating toxic metabolites from the body.
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The microscopic anatomy of the liver is a complex and intricate system that comprises numerous structural units known as liver lobules, each of which is comparable in size to a sesame seed. These hexagonal structures consist of plates of liver cells or hepatocytes, which are characterized by their versatility and abundance of cellular apparatus like rough and smooth ER, Golgi apparatus, peroxisomes, and mitochondria.
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Updated: May 17, 2026

Subtyping of Campylobacter jejuni ssp. doylei Isolates Using Mass Spectrometry-based PhyloProteomics MSPP
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一个集成的CSPPC和BiLSTM框架用于恶意URL检测.

Jinyang Zhou1, Kun Zhang2, Anas Bilal3

  • 1School of Information Science and Technology, Hainan Normal University, Haikou, 571158, Hainan, China.

Scientific reports
|February 24, 2025
PubMed
概括

本研究介绍了CSPPC-BiLSTM,这是一个用于检测恶意URL和增强网络安全的先进模型. 与现有方法相比,新方法显著提高了网络鱼网站检测准确度.

关键词:
这就是BiLSTM.这就是为什么CBAM是CBAM.深度学习是一种深度学习.恶意URL检测恶意URL检测网络鱼 (phishing) 是一种欺诈行为.在 SPP SPP SPP.

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科学领域:

  • 网络安全 网络安全
  • 机器学习 机器学习
  • 深度学习 (Deep Learning) 是一种深度学习.

背景情况:

  • 网络鱼攻击越来越多样化,需要强大的检测方法.
  • 现有的机器学习和深度学习模型用于网络鱼URL检测通常在准确性方面存在局限性.
  • 准确检测恶意URL对于整体网络安全至关重要.

研究的目的:

  • 提出CSPPC-BiLSTM,一种新的恶意URL检测模型.
  • 为了提高网络鱼网站检测的准确性和稳定性.
  • 利用注意力机制和多尺度的聚合来改善特征提取.

主要方法:

  • 使用双向长短期内存 (BiLSTM) 来捕获URL字符序列中的上下文信息.
  • 整合卷积块注意力模块 (CBAM) 以通过通道和空间注意力突出显示关键特征.
  • 使用空间金字塔聚合 (SPP) 进行多级特征提取.
  • 实施掉队规范化,以提高模型的稳定性.

主要成果:

  • 与CharBiLSTM基线相比,CSPPC-BiLSTM显著提高了检测准确度.
  • 该模型在平衡 (Grambedding) 和不平衡 (Mendeley AK Singh 2020 phish) 数据集上显示出强大的概括性和准确性.
  • 废弃实验验证了CBAM和SPP模块对性能提升的关键贡献.

结论:

  • CSPPC-BiLSTM为恶意URL检测提供了一种卓越的方法.
  • 集成CBAM和SPP模块有效地提高了检测性能.
  • 拟议的模型为网络安全提供了更准确,更强大的解决方案,以应对网络鱼攻击.