使用CGAN检测入侵的数据不平衡的研究
Guangyu Zhao1, Peng Liu1, Ke Sun2
1School of Electronic Information Engineering, Changchun University of Science and Technology, Changchun, China.
PloS one
|October 10, 2023
概括
本研究介绍了一种使用条件生成对抗网络 (cGAN) 的新型入侵检测策略,以提高不平衡网络数据的检测准确性. 该方法增强了概括能力,并减少了入侵检测系统 (IDS) 中错过的攻击.
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:
- 传统的入侵检测系统 (IDS) 与不平衡的数据集作斗争,导致攻击类别遗漏和不良概括.
- 处理不平衡的输入数据是保持有效网络安全的一个重大挑战.
研究的目的:
- 提出一种新的入侵检测策略,解决传统IDS在处理不平衡数据方面的局限性.
- 提高概括能力,减少入侵检测中的遗漏错误.
主要方法:
- 为入侵检测开发了一个基于条件生成对抗网络 (cGAN) 的策略.
- cGAN生成合成攻击样本,模拟在有限的间隔内输入数据的分布,避免数据冗余.
- 这种方法旨在减轻机械数据扩展造成的问题.
主要成果:
- 与传统方法相比,拟议的战略显示出优越的绩效指数.
- 实验结果表明,在整体表现方面,一般化能力更强.
- 该战略有效地解决了分类性能不足和检测缺失的问题.
结论:
- 基于cGAN的入侵检测策略为不平衡的数据集提供了强大的解决方案.
- 这种方法显著改善了对网络入侵的检测,并提高了系统的可靠性.
- 这些发现突出了cGAN在网络入侵检测领域的发展潜力.
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