一个增强的整体防御框架,以提高入侵检测系统的对抗性强度
Zeinab Awad1,2, Magdy Zakaria3, Rasha Hassan3
1Department of Computer Science, Faculty of Computers and Information Sciences, Mansoura University, Mansoura, 35516, Egypt. zeinab_awad@mans.edu.eg.
Scientific reports
|April 23, 2025
概括
本研究引入了一个整体防御框架,以保护入侵检测系统 (IDS) 免受敌对攻击. 这种新的方法提高了稳定性,并保持了基于深度神经网络 (DNN) 的网络安全防御的高精度.
科学领域:
- 网络安全 网络安全
- 机器学习 机器学习
- 深度神经网络 深度神经网络
背景情况:
- 深度神经网络 (DNN) 增强了入侵检测系统 (IDS),但很容易受到敌对攻击.
- 现有的防御系统难以平衡稳定性,清洁数据的准确性和流量功能完整性.
研究的目的:
- 为基于DNN的IDS开发和评估一个全面的对抗性防御策略集.
- 提高对抗干扰的弹性,同时保持高检测准确度.
主要方法:
- 实施了两阶段的防御:对抗训练,标签平滑和训练期间的高斯增强.
- 在测试过程中使用了一种排泄稀疏的自动编码器进行预处理,以清除对抗样本.
主要成果:
- 整体防御框架显著提高了对手的稳定性和分类性能.
- 在CICIDS2017和CICIDS2018数据集上实现了87.34% (多数投票) 和98.78% (加权平均) 的总预测准确率.
结论:
- 拟议的框架有效地打击IDS中的对抗性威胁.
- 展示了强大的方法来保护基于DNN的入侵检测系统免受复杂的攻击.
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