用基于新检测的模型对采样网络流量数据进行恶意流量检测
Adrián Campazas-Vega1, Ignacio Samuel Crespo-Martínez2, Ángel Manuel Guerrero-Higueras3
1Robotics Group, University of León, Campus de Vegazana s/n, 24071, León, Spain. acamv@unileon.es.
Scientific reports
|September 18, 2023
概括
网络攻击带来了重大风险. 这项研究表明,新检测模型可以有效地识别恶意网络流量,即使大量采样流量数据,实现高精度和低误报.
科学领域:
- 计算机科学 计算机科学
- 网络安全 网络安全
- 网络安全 网络安全
背景情况:
- 经典的异常检测分析单个数据包,这对于高流量路由器来说是不可行的.
- 路由器使用流量数据进行网络统计,但由于计算成本,需要采样,从而导致信息丢失.
- 使用采样流量数据检测网络攻击仍然是一个挑战.
研究的目的:
- 通过对采样流量数据的异常检测来证明检测恶意网络流量的可行性.
- 评估基于新性检测的模型在流量数据上的性能,采样率为1000个包中的1个.
- 用合成和现实世界的网络数据评估这些模型的准确性和错误报警率.
主要方法:
- 利用基于异常检测的模型,特别关注新奇的检测.
- 采用来自RedCAYLE网络的合成采样流量数据和实际采样流量数据.
- 基于准确度和错误报警率评估模型性能.
主要成果:
- 恶意网络流量可以被成功检测,即使使用流量数据采样在1000个包中的1个的速度.
- 基于新性检测的模型在采样流量数据中识别恶意流量方面取得了高准确性.
- 拟议的方法显示了低虚假报警率,表明可靠的检测.
结论:
- 异常检测,特别是新奇的检测,是有效的识别网络攻击在采样网络流量数据.
- 这些发现支持使用采样流数据用于在资源有限的环境中检测网络安全威胁.
- 这项研究证实,尽管采用了积极的数据采样,但仍然可以保留重要的网络安全见解.
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