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.
Sensors (Basel, Switzerland)
|December 9, 2023
概括
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.
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.
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