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从异常样本通过负选择生成探测器,用于检测网络入侵
Zhiyong Li1, Xiang Wei2, Chunyan Li2
1School of Engineering, Honghe University, Mengzi, Yunnan Province, 661199, China. lizhiyong@uoh.edu.cn.
Scientific reports
|October 17, 2025
概括
本研究引入了一种改进的负选择算法 (NSA),用于网络异常检测. 通过使用异常样本作为中心,它增强了低维空间中的探测器生成,提高了关键数据集的性能.
科学领域:
- 计算机科学 计算机科学
- 网络安全 网络安全
- 人工智能的人工智能
背景情况:
- 网络异常检测对于网络安全至关重要.
- 传统的负选择算法 (NSA) 难以处理高维特征空间和低维数据度.
- 尺寸不匹配限制了现有的NSA的有效性.
研究的目的:
- 改进网络异常检测,使用新的NSA方法.
- 为了解决传统NSA的维度不匹配问题.
- 加强在相关的低维子空间中生成成熟的探测器.
主要方法:
- 作为候选探测器中心,利用培训数据中的未充分利用的异常样本.
- 利用异常样本在低维子空间内引导探测器生成.
- 基于最近邻居属性的二次分类来缓解错误分类.
主要成果:
- 与其他八种算法相比,拟议的方法显示出更高的性能.
- 在NSL-KDD和UNSW-NB15网络异常检测数据集上取得了显著的改进.
- 在相关的低维子空间内有效生成成熟的探测器.
结论:
- 新的NSA方法有效地解决了传统方法的局限性.
- 使用异常样本作为中心可以提高探测器生成和网络异常检测的准确性.
- 该方法显示了对现实世界网络安全应用的巨大潜力.
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