构建VAE-GRU-XGBoost入侵检测模型,用于网络安全
Yu Chen1, Xiaohong Zheng1, Nan Wang1
1Zhangjiakou Open University, Zhangjiakou, China.
PloS one
|June 25, 2025
概括
这项研究引入了一种新的深度学习模型,用于先进的网络入侵检测. 该模型有效地识别复杂的网络威胁,以高准确度提高整体网络安全.
科学领域:
- 网络安全 网络安全
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 越来越多的大数据扩大了网络安全威胁.
- 复杂的网络攻击需要先进的防御机制.
- 深度学习为网络入侵检测提供了有前途的解决方案.
研究的目的:
- 使用深度学习开发一个强大的网络入侵检测模型.
- 提高识别网络入侵的准确性和效率.
- 为了应对复杂的网络攻击所带来的挑战.
主要方法:
- 使用变量自动编码器 (VAE) 来提取特征和减少网络流量的维度.
- 集成的极端梯度提升 (XGBoost) 用于高效的分类任务.
- 与VAE和XGBoost结合的Gated Recurrent Units (GRUs) 来构建最终的入侵检测模型.
主要成果:
- 在KDD99数据集上达到97.48%的曲线下的面积 (AUC),在OODS数据集上达到95.24%.
- 在训练和测试组中,各种攻击流量样本的分类准确度超过0.91.
- 报告的高效特征提取时间,在样本大小为10,000和40,000的样本中,从0.030s到0.112s不等.
结论:
- 开发的深度学习模型基于改进的变量自动编码器,在网络入侵检测中提供高精度.
- 该模型有效地处理复杂的网络攻击,为网络安全做出重大贡献.
- VAE,XGBoost和GRU的整合为实时威胁识别提供了一个强大的框架.
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