一组功能增强的卷积神经网络和深度自动编码器,用于有效检测网络攻击
Selvakumar B1, Sivaanandh M2, Muneeswaran K2
1Department of Computer Science and Engineering, Mepco Schlenk Engineering College, Sivakasi, 626005, India. selvakumar.b@mepcoeng.ac.in.
Scientific reports
|February 5, 2025
概括
这项研究引入了用于网络入侵检测系统 (NIDS) 的新型深度学习组合,显著提高了数据包流分类准确性. 这种先进的方法提高了对关键的低频网络威胁的检测率.
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:
- 有效的网络流量监控对于检测入侵和网络攻击至关重要.
- 现有的网络入侵检测系统 (NIDS) 需要提高数据包流分类效率.
研究的目的:
- 提出一种新的深度学习技术组合,以改善NIDS中的数据包流分类.
- 加强在网络安全数据集中的少数攻击类别的检测.
主要方法:
- 开发了一个三阶段的方法:特征增强卷积神经网络 (FA-CNN),深度自编码器,以及两者的合奏.
- 美国广播公司 (FA-CNN) 使用通过相互信息选择的增强功能.
- 组装FA-CNN与深度自动编码器,提供强大的分类模型.
主要成果:
- 对NSL-KDD和CICIDS2017数据集的实验验证表明,与现有方法相比,其性能优越.
- 在NSL-KDD数据集上达到97%的整体准确性,在CICIDS2017数据集上达到95%.
- 显著提高了少数攻击类的检测率,例如U2R (NSL-KDD) 和Heartbleed (CICIDS2017).
结论:
- 拟议的深度学习组合方法在NIDS数据包流分类方面取得了重大进展.
- 该方法有效地解决了检测低频,高影响网络攻击的挑战.
- 这项工作为实时网络安全监控提供了更准确,更有效的解决方案.
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