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多目标海优化算法与深度学习支持的安全云环境的漏洞检测.

Mohammed Aljebreen1, Manal Abdullah Alohali2, Hany Mahgoub3

  • 1Department of Computer Science, Community College, King Saud University, P.O. Box 28095, Riyadh 11437, Saudi Arabia.

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概括
此摘要是机器生成的。

本研究介绍了一种新的多目标海优化算法,具有深度学习支持的漏洞检测 (MOSOA-DLVD),以提高云安全性. 在检测云入侵时,MOSOA-DLVD技术达到99.34%的准确性.

关键词:
云计算是云计算中的一个.深度学习是一种深度学习.侵入检测系统的入侵检测系统海优化算法海优化算法苏蒂特恩优化算法 苏蒂特恩优化算法

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科学领域:

  • 云计算安全 云计算安全
  • 网络安全 网络安全
  • 机器学习应用程序 机器学习应用程序

背景情况:

  • 云计算提供了具有成本效益的服务,但也面临着重大的安全挑战.
  • 侵入检测系统 (IDS) 对于识别正常和异常网络行为至关重要.
  • 机器学习 (ML) 技术通过学习模式和预测结果来增强IDS.

研究的目的:

  • 为云平台安全设计一种新的多目标海优化算法,并支持深度学习的漏洞检测 (MOSOA-DLVD).
  • 在云环境中增强入侵检测和分类准确性.
  • 通过超参数调整来提高深信网络 (DBN) 算法的性能.

主要方法:

  • 通过使用多目标海优化算法 (MOSOA) 实现特征选择 (FS) 方法.
  • 使用深度信任网络 (DBN) 进行入侵检测和分类.
  • 应用了灰天优化算法 (STOA) 来对DBN进行超参数调整,以提高检测准确性.

主要成果:

  • 在云基础设施内,MOSOA-DLVD技术在识别漏洞和攻击方面表现出了很高的能力.
  • 在基准IDS数据集上实现了99.34%的最大入侵检测准确度.
  • 在入侵检测方面表现优于最近的方法,确定了该模型的有效性.

结论:

  • 拟议的MOSOA-DLVD技术为保护云平台免受网络威胁提供了强大的解决方案.
  • 整合MOSOA用于特征选择和STOA用于DBN超参数调整,显著提高了检测准确性.
  • 该研究验证了MOSOA-DLVD系统通过先进的ML技术来增强云安全的能力.