在Kubernetes环境中实时多类威胁检测和自适应欺骗
Abdelrahman Aly1, Ahmed M Hamad2, Mirvat Al-Qutt2
1Faculty of Computer and Information Science, Ain Shams University, Cairo, Egypt. abdlrhmn.ali@cis.asu.edu.eg.
Scientific reports
|March 15, 2025
概括
本研究介绍了Kubernetes安全的新框架,使用机器学习和欺骗来检测和减轻网络威胁. 它增强云原生防御,具有高精度和有效的攻击者参与.
科学领域:
- 网络安全 网络安全
- 云计算 云计算 云计算
- 机器学习 机器学习
背景情况:
- 对于云原生应用程序来说,Kubernetes至关重要,但对特权升级和DoS攻击等网络威胁很容易受到攻击.
- 现有的安全措施很难跟上Kubernetes环境的动态性.
- 需要积极主动和适应性安全解决方案来保护容器化应用程序.
研究的目的:
- 通过实时威胁检测和自适应欺骗来增强Kubernetes安全的新型框架.
- 整合机器学习以进行多类威胁分类和动态诱部署.
- 评估框架在减轻威胁和保持系统弹性方面的有效性.
主要方法:
- 集成KServe用于可扩展的机器学习威胁分类.
- 使用CICFlowMeter进行网络流量特征提取.
- 实现KubeDeceive用于动态诱部署,由MAPE-K控制循环管理.
主要成果:
- 实现了高威胁检测准确度,达到高达91%.
- 证明了高效的资源利用和有效的攻击者参与,诱成功率高达93%.
- 为主动减轻威胁提供可操作的情报.
结论:
- 拟议的框架为Kubernetes提供了一个可扩展和适应的防御机制.
- 它有效地提高了动态云基础设施的安全性和弹性.
- 检测和欺骗的统一方法为对抗复杂的网络威胁提供了强大的解决方案.
关键词:
适应性安全是适应性的安全.云原生基础设施是云原生基础设施.网络欺骗 网络欺骗在 KServe 服务.库伯内特安全 Kubernetes安全多类检测检测多类检测网络流量分析 网络流量分析实时威胁检测和威胁检测.更多相关视频
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