支持向量数据描述与内核密度估计 (SVDD-KDE) 控制图用于网络入侵监控
Muhammad Ahsan1, Hidayatul Khusna2, Wibawati2
1Department of Statistics, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia. muh.ahsan@its.ac.id.
Scientific reports
|November 6, 2023
概括
这项研究引入了一个新的SVDD-KDE控制图用于网络入侵检测,有效处理非正常数据并减少错误报警. 拟议的方法在检测异常和网络攻击方面表现出卓越的性能.
科学领域:
- 统计过程控制 统计过程控制
- 机器学习用于网络安全
背景情况:
- 多变量控制图表对于网络入侵检测至关重要,但与非正常的网络流量数据作斗争,导致虚假报警.
- 传统方法需要数据跟随正常分布,这限制了它们在现实世界网络监控中的有效性.
研究的目的:
- 提出一种新的多变量控制图,SVDD-KDE,旨在有效监控非正常数据分布中的网络异常.
- 通过提高准确性和减少假阳性来增强网络入侵检测系统 (IDS).
主要方法:
- 支持矢量数据描述 (SVDD) 与内核密度估计 (KDE) 的集成,以创建非参数控制图.
- 使用SVDD距离用于异常检测和KDE用于估计非正常数据中的控制极限.
- 使用合成数据集进行模拟研究,并对用于IDS评估的NSL-KDD基准数据集进行分析.
主要成果:
- 与传统方法相比,SVDD-KDE图表在检测多变量数据转移方面表现更好,在识别异常值方面具有更高的准确性.
- 当应用到IDS时,SVDD-KDE图表实现了高精度 (0.917) 和AUC (0.915) 与低假阳性率.
- 该方法在监控网络攻击方面被证明是有效的,超过了其他几种基于IDS的控制图表和机器学习算法.
结论:
- 拟议的SVDD-KDE多变量控制图是网络入侵检测的强有力的解决方案,特别是对于非正常数据.
- 这种方法显著提高了网络流量的异常检测能力,为网络安全提供了有价值的工具.
- 尽管计算成本很高,但SVDD-KDE方法提供了可靠和准确的IDS,并降低了假阳性率.
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