使用基于混合图的卷积网络和变压器架构进行网络入侵检测
Peter Appiahene1, Samuel Opoku Berchie1, Emmanuel Botchway1
1Department of Information Technology and Decision Sciences, University of Energy and Natural Resources, Sunyani, Ghana.
PloS one
|January 21, 2026
概括
一个新的混合入侵检测模型,GConvTrans,有效地识别了云环境中复杂的网络威胁. 这种先进的系统结合了图形卷积和变压器层,用于强大的网络入侵检测.
科学领域:
- 计算机科学 计算机科学
- 网络安全 网络安全
- 人工智能的人工智能
背景情况:
- 云计算的扩张增加了对先进网络攻击的脆弱性.
- 传统的安全系统与复杂的入侵作斗争.
- 现有的基于人工智能的入侵检测模型面临着数据和动态模式识别的局限性.
研究的目的:
- 为云环境开发一种新的混合入侵检测模型.
- 解决当前人工智能模型在检测复杂网络入侵方面的局限性.
- 通过使用深度学习来增强网络入侵检测系统 (NIDS).
主要方法:
- 提出了一个混合深度神经网络架构,命名为GConvTrans.
- 集成图形卷积层和变压器编码器层.
- 使用CIC-IDS 2018数据集将表格式网络流量数据转化为计算图表.
主要成果:
- GConvTrans实现了高精度:84.7%的培训,96.75%的验证和96.94%的测试集.
- 该模型有效地利用了当地结构信息和全球背景.
- 证明了将图形学习与深度学习相结合用于入侵检测的稳定性.
结论:
- 在云环境中,GConvTrans模型在检测复杂网络入侵方面显示出显著的前景.
- 结合图形学习和深度学习技术,为网络安全提供了强大的方法.
- 未来的工作包括探索其他数据集,完善架构,并分析像链接预测这样的图形学习任务的性能.
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