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Masking and Demasking Agents01:19

Masking and Demasking Agents

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 the metal...
Neural Circuits01:25

Neural Circuits

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...
Convolution Properties II01:17

Convolution Properties II

The important convolution properties include width, area, differentiation, and integration properties.
The width property indicates that if the durations of input signals are T1 and T2, then the width of the output response equals the sum of both durations, irrespective of the shapes of the two functions. For instance, convolving two rectangular pulses with durations of 2 seconds and 1 second results in a function with a width of 3 seconds.
The area property asserts that the area under the...

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相关实验视频

Updated: May 10, 2026

Focal Ca2+ Transient Detection in Smooth Muscle
17:41

Focal Ca2+ Transient Detection in Smooth Muscle

Published on: June 29, 2009

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使用卷积神经网络的堆叠检测脆弱的JavaScript函数.

Abdullah Sheneamer1

  • 1Computer Science Department, Jazan University, Jazan, Saudi Arabia.

PeerJ. Computer science
|March 4, 2024
PubMed
概括
此摘要是机器生成的。

这项研究引入了一组新的卷积神经网络 (CNN) 来检测易受攻击的JavaScript函数,显著提高了Web应用程序的安全性. 堆叠的CNN方法在识别代码漏洞时达到约98%的准确性.

关键词:
代码安全代码安全代码跨站点脚本检测检测.这是JavaScript引擎的漏洞.网络应用程序的安全性堆叠卷积神经网络 (CNN) 是一种神经网络.将CNN的学习转移到CNN学习.易受攻击的JavaScript函数检测检测

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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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相关实验视频

Last Updated: May 10, 2026

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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
  • 软件工程 软件工程 软件工程

背景情况:

  • 网络应用程序的安全性对于防止网络攻击至关重要.
  • 静态分析是识别软件漏洞的常用方法.
  • 目前用于检测易受攻击的JavaScript代码的现有方法往往具有较低的准确性和较高的错误正/负值.

研究的目的:

  • 提出一种新的方法来识别使用卷积神经网络 (CNN) 的集合识别脆弱的JavaScript函数.
  • 提高网络应用程序中检测安全漏洞的准确性和可靠性.
  • 为了解决JavaScript代码当前静态分析技术的局限性.

主要方法:

  • 开发一组卷积神经网络 (CNN) 模型.
  • 使用易受攻击的代码信息和代码特征进行检测.
  • 实施一个堆叠的CNN方法,结合采样技术 (失衡,随机低采样,随机过采样).
  • 在公开可用的JavaScript代码块上训练和评估模型.

主要成果:

  • 堆叠的CNN方法在检测易受攻击的JavaScript函数方面表现出了很高的有效性.
  • 拟议的方法的准确率约为98%.
  • 该方法显示了对现实世界的安全应用程序的稳定性和可用性.

结论:

  • 合奏CNN模型在识别易受攻击的JavaScript代码的现有方法上提供了显著的改进.
  • 这项研究为保护网络应用程序免受网络威胁提供了更有效的解决方案.
  • 通过提高系统安全性,这些发现有助于在线环境更安全.