在无服务器架构中生成用于DoW攻击检测的数据集
José Manuel Ortega Candel1, Francisco José Mora Gimeno1, Higinio Mora Mora1
1Department of Computer Science and Technology, Alicante University, Alicante, Spain.
Data in brief
|December 14, 2023
概括
这项研究引入了一个新的数据集,以帮助检测无服务器计算中的拒绝钱包 (DoW) 攻击. 该数据集有助于开发机器学习模型,以识别这些昂贵的网络威胁.
科学领域:
- 云计算安全 云计算安全
- 网络安全 网络安全
- 数据科学数据科学数据科学
背景情况:
- 拒绝钱包 (DoW) 攻击利用无服务器环境,造成过高的成本.
- 无服务器架构 (Function-as-a-Service) 提供了可扩展性,但容易受到DoW攻击.
- 缺乏现实的数据集阻碍了DoW攻击检测的研究.
研究的目的:
- 创建一个合成数据集,用于模拟无服务器架构中的函数调用.
- 促进用于DoW攻击检测的机器学习模型的开发和评估.
- 为无服务器安全研究人员和开发人员提供一个有价值的资源.
主要方法:
- 基于真实的云流量模式生成合成数据.
- 识别了函数调用的关键特征:提交时间,调用延迟,响应延迟,函数持续时间,请求时的主动函数和响应时的主动函数.
- 提出了一种数据集生成方法及其在攻击检测中的应用.
主要成果:
- 创建了一个新的数据集,模拟无服务器函数调用.
- 数据集捕捉了分析无服务器流量的基本特征.
- 该数据集可在门德利数据库中公开使用.
结论:
- 生成的数据集对于推进在无服务器环境中检测拒绝钱包攻击的研究至关重要.
- 使用此数据集,可以有效地应用机器学习技术和神经网络.
- 该资源将提高无服务器应用程序的可靠性,效率和安全性.
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