基于CE-GAN的方法来解决网络入侵检测系统中的数据不平衡问题
Yang Yang1,2,3, Xiaoyan Liu1, Dianli Wang1
1Changchun Sci-Tech University, Changchun, 130022, China.
Scientific reports
|March 6, 2025
概括
本研究介绍了一种新的条件生成对抗网络 (CE-GAN),用于解决网络入侵检测系统中的数据不平衡问题. CE-GAN 提高了对复杂网络威胁的分类器性能和稳定性.
科学领域:
- 网络安全 网络安全
- 人工智能的人工智能
- 网络安全 网络安全
背景情况:
- 传统的入侵检测系统与复杂的网络行为和不平衡的数据集作斗争.
- 数据失衡严重阻碍了现有的网络入侵检测模型的性能和稳定性.
- 游戏理论概念为改善安全应用中的分类器组合优化提供了潜力.
研究的目的:
- 提出一种新的网络入侵检测系统,有效地解决数据不平衡问题.
- 使用先进的人工智能技术,提高入侵检测系统的稳定性和分类性能.
- 为了提高网络安全,利用游戏理论和生成对抗网络.
主要方法:
- 介绍了纳什平衡概念从游戏理论到分类器组合优化.
- 开发了一个带有条件聚合编码器-解码器结构 (CE-GAN) 的条件生成对抗网络.
- 使用复合损失函数来确保生成的网络流量样本的真实性和多样性.
主要成果:
- 在不平衡的数据集中,CE-GAN有效地增强了罕见数据样本.
- 在NSL-KDD和UNSW-NB15数据集中观察到分类指标的显著改善.
- 与传统方法相比,拟议的CE-GAN模型在网络入侵检测方面表现优异.
结论:
- CE-GAN模型为网络入侵检测提供了强大的解决方案,特别是在数据不平衡的场景中.
- 整合游戏理论和先进的生成模型可以增强网络安全系统的能力.
- 这种方法在有效检测复杂网络入侵方面取得了重大进展.
相关概念视频
01:25Strategies for Assessing and Addressing Confounding
79
Confounding is a critical issue in epidemiological studies, often leading to misleading conclusions about associations between exposures and outcomes. It occurs when the relationship between the exposure and the outcome is mixed with the effects of other factors that influence the outcome. Given that, addressing confounding is of high importance for drawing accurate inferences in research.
Confounding can be addressed at both the design phase of a study and through analytical methods after data...
Confounding can be addressed at both the design phase of a study and through analytical methods after data...
79
01:02Quantifying and Rejecting Outliers: The Grubbs Test
1.4K
Sometimes, a data set can have a recorded numerical observation that greatly deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier. To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
1.4K
01:00Detection of Gross Error: The Q Test
5.1K
When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...
5.1K
01:23Mechanistic Models: Compartment Models in Individual and Population Analysis
23
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
23