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在基于新型深度学习方法的Web应用程序中检测命令注入攻击.

Xinyu Wang1, Jiqiang Zhai2, Hailu Yang1

  • 1School of Computer Science and Technology, Harbin University of Science and Technology, Harbin, 150006, China.

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概括

一个新的深度学习模型,Convolutional Channel-BiLSTM Attention (CCBA),可以有效地检测网络命令注入攻击. 这种先进的方法提高了准确性和回忆力,为Web应用程序安全提供了强大的解决方案.

关键词:
攻击检测检测攻击检测深度学习是一种深度学习.网络命令注入 Web命令注入

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科学领域:

  • 网络安全 网络安全
  • 人工智能的人工智能
  • 深度学习 (Deep Learning) 是一种深度学习.

背景情况:

  • 网络命令注入攻击威胁到Web应用程序,导致数据泄露和服务中断.
  • 现有的检测方法面临着复杂,模糊的攻击的挑战,导致效率低,准确性差.
  • 先进的特征提取和时间分析对于有效的检测至关重要.

研究的目的:

  • 提出一种新的深度学习模型,用于增强识别网络命令注入攻击.
  • 提高在Web应用程序中检测复杂恶意代码的准确性和效率.
  • 解决传统检测方法在处理复杂攻击模式方面的局限性.

主要方法:

  • 开发了卷积通道-BiLSTM注意力 (CCBA) 模型,集成了深度学习技术.
  • 使用双CNN卷积通道进行全面的特征提取.
  • 采用BiLSTM网络用于双向时间特征识别和特征加权的注意力机制.

主要成果:

  • 在现实数据集上,CCBA模型实现了99.3%的准确性和98.2%的回忆率.
  • 两个公共网络安全数据集的准确度超过98%,表现一致,证明了稳定性和通用性.
  • 在识别网络命令注入攻击方面表现优于现有的方法.

结论:

  • CCBA模型为检测网络命令注入攻击提供了一个非常有效的解决方案.
  • 深度学习,特别是CCBA架构,显著提高了Web应用程序的安全性.
  • 该模型的卓越性能验证了其对现实世界的网络安全应用的潜力.