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对于基于机器学习的网络入侵检测系统,用于检测受污染的训练数据集的方法
Joaquín Gaspar Medina-Arco1, Roberto Magán-Carrión1, Rafael Alejandro Rodríguez-Gómez1
1Network Engineering & Security Group (NESG), University of Granada, 18012 Granada, Spain.
Sensors (Basel, Switzerland)
|January 23, 2024
概括
本研究引入了一种新的方法,通过识别和纠正训练集中错误标记的数据来改进网络入侵检测系统 (NIDS),提高对网络威胁的异常检测准确性.
科学领域:
- 网络安全 网络安全
- 机器学习 机器学习
- 网络安全 网络安全
背景情况:
- 网络入侵检测系统 (NIDS) 对于检测网络攻击至关重要.
- 基于异常的NIDS依赖于在标记数据集上训练的机器学习模型.
- 在训练集中错误标记的数据可以显著降低NIDS的性能.
研究的目的:
- 解决基于异常的NIDS中错误标记的网络流量数据集的挑战.
- 开发一种分析数据集质量和识别隐藏异常的方法.
- 通过选择理想的训练子集来优化NIDS性能,即使有错误标记的数据.
主要方法:
- 提出了一种新的两步方法来分析网络流量数据集质量.
- 该方法可以识别现有数据集中的隐藏或未识别的异常.
- 它涉及数据子集的增量选择来训练异常检测模型.
主要成果:
- 拟议的方法成功地在受污染的UGR'16数据集中识别了隐藏的尸网络攻击.
- 实验证明了揭示错误标记数据的可行性,包括标记为异常的正常流量.
- 这种方法提高了最先进的NIDS (Kitsune) 在受损数据集上的性能.
结论:
- 开发的方法有效地提高了基于异常的NIDS对数据集错误标记的稳定性.
- 它提供了一种可靠的方式来提高入侵检测系统的准确性.
- 这种方法对于在不断变化的网络威胁面前保持有效的网络安全至关重要.
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