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基于多模式融合的少数拍摄网络入侵检测系统.
Congyuan Xu1,2, Yong Zhan3, Zhiqiang Wang4
1College of Information Science and Engineering, Jiaxing University, Jiaxing, Zhejiang, China. cyxu@zjxu.edu.cn.
Scientific reports
|July 2, 2025
概括
这项研究引入了一种新的多式联络融合方法,用于为数次的网络入侵检测. 它通过结合流量和网络特征,有效地提高了罕见攻击的检测准确性.
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:
- 越来越复杂的网络环境和不断变化的攻击载体需要先进的入侵检测系统.
- 传统的方法在新的或罕见的网络攻击中扎着有限的标记数据,阻碍了少量射击学习的有效性.
- 现有的少数射击学习模型通常依赖于单模数据,未能在不同类型的数据中利用互补信息.
研究的目的:
- 开发一种基于多式联接的少数拍摄网络入侵检测方法.
- 应对有限的培训数据的挑战,以检测新型和罕见的网络攻击.
- 通过整合各种数据模式来提高网络入侵检测性能.
主要方法:
- 提出了一种多式联络融合方法,将交通特征图和网络特征集结合起来.
- 开发了使用卷积神经网络进行交通特征图的G模型和使用变压器进行网络特征集的S模型.
- 研究了融合策略在各种相互作用深度对增强检测的影响.
主要成果:
- 在CICIDS2017数据集上实现了93.40%的多类准确率.
- 在CICIDS2018数据集上实现了98.50%的多类准确率.
- 与现有的几次射击网络入侵检测方法相比,其表现卓越.
结论:
- 多式联络融合方法显著提高了少数拍摄网络入侵检测能力.
- 集成流量特征图和网络特征集有效地解决了罕见攻击的数据稀缺问题.
- 拟议的G型和S型融合战略为先进的网络安全提供了强大的解决方案.
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