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基于无监督学习和开放式识别的未知入侵流量检测方法.

Jun Fang¹, Cunxiang Xie²

¹Naval Aviation University, Yantai, 264001, China.

Scientific reports

|May 16, 2025

概括

此摘要是机器生成的。

本研究介绍了一种新的开放式入侵检测模型,使用生成对抗网络和OpenMax. 该模型有效地以高准确度识别未知网络流量,增强网络安全防御.

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Last Updated: May 20, 2025

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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:

侵入检测系统 (IDS) 对网络安全和用户隐私至关重要.
目前的IDS经常受到低分类准确度和专注于封闭式检测的困扰,无法识别新的威胁.
需要强大的方法来检测和分类未知的 (开放式) 网络流量.

研究的目的:

为开放式网络流量提出一种新的检测和分类模型.
解决现有的入侵流量检测算法的局限性,特别是它们的低准确性和封闭性质.
提高入侵检测系统识别以前未见的恶意网络活动的能力.

主要方法:

开发一个开放式流量检测和分类模型,集成信息最大化生成对抗网络 (GM) 和OpenMax算法.
在封闭条件下训练入侵流量分类模型.
使用OpenMax算法重新计算样本激活向量,以估计未知流量类别的概率.

主要成果:

拟议的模型在CICIDS2017对开放集流量的数据集上实现了高分类准确性.
误用检测准确度超过了88.5%,异常检测准确度超过了88.2%.
该模型在检测各种类型的未知流量方面表现出有效性,具有显著的准确性和稳定性.

结论:

开发的模型为开放式入侵流量检测和分类提供了有效的解决方案.
整合GM和OpenMax可以更好地识别未知的网络威胁.
这些发现有助于提高网络通信安全性,并保护用户信息隐私免受不断变化的网络威胁.