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相关实验视频

Updated: Jun 19, 2025

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

381

使用蜘蛛优化算法优化神经网络,用于入侵检测系统的优化.

Deepshikha Kumari1, Abhinav Sinha1, Sandip Dutta1

  • 1Department of Computer Science and Engineering, Birla Institute of Technology, Mesra, Ranchi, Jharkhand, 835215, India.

Scientific reports
|July 26, 2024
PubMed
概括

本研究介绍了一种新的蜘蛛优化-人工神经网络 (SMO-ANN) 模型,用于增强网络威胁检测. 该SMO-ANN模型在识别恶意网络流量方面实现了高准确性,改善了网络安全防御.

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科学领域:

  • 网络安全和人工智能 人工智能
  • 网络入侵检测系统

背景情况:

  • 黑客攻击,网络鱼和数据泄露等网络威胁对全球个人和组织构成重大风险.
  • 侵入检测系统对于识别异常网络流量和实时警告恶意活动至关重要.

研究的目的:

  • 开发和评估一个优化的人工神经网络 (ANN) 模型来检测网络攻击.
  • 通过先进的优化技术,提高入侵检测系统的准确性和效率.

主要方法:

  • 使用蜘蛛优化 (SMO) 算法优化人工神经网络 (ANN) 层.
  • 开发SMO-ANN模型,将网络流量分类为良性或恶意.
  • 使用各种数据集对SMO-ANN模型的评估:Luflow,CIC-IDS 2017,UNR-IDD,以及NSL-KDD.

主要成果:

  • 在分类网络流量方面,SMO-ANN模型表现出了卓越的性能.
  • 在二进制Luflow数据集上实现了100%的准确性.
  • 在多类NSL-KDD数据集上实现了99%的准确性,表明了强大的检测能力.

结论:

  • 拟议的SMO-ANN模型为实时入侵检测提供了一个高度有效的解决方案.
关键词:
网络安全 网络安全 网络安全深度学习是一种深度学习.侵入检测系统的入侵检测系统蜘蛛的优化 蜘蛛的优化

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  • 这项研究通过提供准确有效的识别网络威胁的方法,有助于推进网络安全.