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相关概念视频

Detection of Black Holes01:10

Detection of Black Holes

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Although black holes were theoretically postulated in the 1920s, they remained outside the domain of observational astronomy until the 1970s.
Their closest cousins are neutron stars, which are composed almost entirely of neutrons packed against each other, making them extremely dense. A neutron star has the same mass as the Sun but its diameter is only a few kilometers. Therefore, the escape velocity from their surface is close to the speed of light.
Not until the 1960s, when the first neutron...
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Classification of Neurotransmitters01:30

Classification of Neurotransmitters

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Neurotransmitters play a crucial role in the communication between neurons in the autonomic nervous system. Neurons in the autonomic nervous system can be cholinergic or adrenergic depending on the neurotransmitters synthesized. Cholinergic neurons use acetylcholine as their primary neurotransmitter. This includes all the preganglionic fibers of the sympathetic and pre- and postganglionic fibers of the parasympathetic nervous systems. In addition, neurons of the somatic nervous system also use...
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Neural Circuits01:25

Neural Circuits

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Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
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Masking and Demasking Agents01:19

Masking and Demasking Agents

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EDTA titrations may necessitate masking and demasking agents to temporarily protect a particular metal ion in a mixture from the EDTA reaction. These agents facilitate the sequential analysis of the metal ions by forming stable complexes with some—but not all—metal ions during certain steps.
There are many masking agents, such as cyanide, fluoride, triethanolamine, thiourea, and 2,3-bis(sulfanyl)propan-1-ol (formerly 2,3-dimercapto-1-propanol), with the masking agent chosen based on...
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Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
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MVDroid:一个使用神经网络的安卓恶意VPN检测器.

Saeed Seraj1, Siavash Khodambashi2, Michalis Pavlidis1

  • 1School of Architecture, Technology and Engineering, University of Brighton, Brighton, BN2 4GJ UK.

Neural computing & applications
|June 26, 2023
PubMed
概括

许多虚拟私人网络 (VPN) 损害了用户的隐私和安全. 这项研究引入了一个深度学习模型,以准确识别恶意的Android VPN,保护用户免受数据盗窃和恶意软件的侵害.

关键词:
安卓恶意软件检测检测 安卓恶意软件检测神经网络的神经网络的神经网络这就是为什么VPN VPN VPN.虚拟私有网络是一个虚拟私有网络.

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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:

  • 虚拟私人网络 (VPN) 广泛用于在线隐私,但许多网络存在重大安全风险.
  • 恶意VPN可以窃取数据,传播恶意软件,并破坏用户隐私,尽管它们看起来合法.
  • 由于不值得信赖的VPN应用程序的普及,Android用户面临着特殊的风险.

研究的目的:

  • 开发一个优化的深度学习模型来检测恶意的Android VPN.
  • 创建一个新的数据集,包括恶意和良性Android VPN应用程序,用于培训和评估.
  • 通过识别和标记不安全的VPN来增强Android用户的安全性.

主要方法:

  • 一个优化的深度学习神经网络被设计和实施.
  • 该模型使用新编辑的Android VPN数据集进行了训练和评估.
  • 应用程序权限被用作识别恶意VPN行为的关键功能.

主要成果:

  • 拟议的深度学习分类器在识别恶意VPN方面取得了很高的准确性.
  • 与标准分类器相比,该模型在准确性,精度和回忆等关键指标上表现出卓越的性能.
  • 实验结果验证了深度学习方法在检测不安全VPN方面的有效性.

结论:

  • 开发的深度学习模型为识别恶意Android VPN提供了可靠的解决方案.
  • 这项研究有助于改善通过VPN寻求隐私的移动用户的安全环境.
  • 这些发现强调了强有力的安全措施在VPN应用程序的重要性.