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Updated: Sep 15, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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基于网络的入侵检测使用深度学习技术.

Muhammad Farhan1, Hafiz Waheed Ud Din1, Saadat Ullah1

  • 1Department of Computing and Information Technology, Faculty of Computing, Gomal University, Dera Ismail Khan, 29050, Pakistan.

Scientific reports
|July 15, 2025
PubMed
概括
此摘要是机器生成的。

使用序列深度神经网络 (DNN) 和额外树分类器的新深度学习模型显著改善了网络入侵检测. 该系统通过减少功能来实现高精度和更快的速度,为网络安全提供了有前途的解决方案.

关键词:
激活功能 激活功能深度神经网络是一种深度神经网络.额外的树木分类器网络攻击 网络攻击基于网络的入侵检测系统 (NIDS).

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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
  • 人工智能的人工智能

背景情况:

  • 网络流量的快速增长和复杂的网络威胁需要先进的入侵检测系统.
  • 传统的基于网络的入侵检测系统 (NIDS) 由于过时的数据和传统的机器学习模型,与新的攻击模式作斗争.
  • 现有的NIDS在检测不断变化的威胁方面面临局限性,这凸显了需要更强大,更适应性的解决方案的需要.

研究的目的:

  • 引入一种新的深度学习方法,用于增强网络入侵检测.
  • 通过集成顺序深度神经网络 (DNN) 与修正线性单位 (ReLU) 激活和特征选择的额外树分类器来解决传统NIDS的局限性.
  • 提高NIDS的准确性,可解释性和计算效率,以实时检测威胁.

主要方法:

  • 开发了一种混合模型,将顺序深度神经网络 (DNN) 与修正线性单元 (ReLU) 激活相结合.
  • 采用额外树分类器来优化功能选择,将功能空间从43个减少到8个高度相关的功能.
  • 使用全面的UNSW-NB15数据集训练和验证模型,模拟现实的网络流量和攻击向量.

主要成果:

  • 拟议的顺序DNN模型实现了高性能指标:97.93%的准确性,97%的精度,97%的回忆率和97%的F1分数用于二进制分类 (正常与攻击).
  • 通过Extra Tree Classifier优化功能,显著降低了计算负载,并提高了推断速度,而不会影响准确性.
  • 实验验证,包括ROC曲线和混矩阵,与现有研究相比,表现优越.

结论:

  • 基于ReLU的DNN与优化的特征选择的集成为网络入侵检测提供了强大的和可解释的解决方案.
  • 拟议的模型有效地克服了诸如消失梯度和过拟合等挑战,这对于可靠的NIDS部署至关重要.
  • 这种先进的NIDS为加强金融,医疗保健和政府等关键基础设施部门的网络安全提供了有希望的解决方案.