通过自我注意机制和代改进提高了通过自我注意机制和代改进提高了少数射击网络入侵检测
Congyuan Xu1,2, Yong Zhan3, Guanghui Chen3
1College of Information Science and Engineering, Jiaxing University, Jiaxing, Zhejiang, China.
PloS one
|January 17, 2025
概括
这项研究介绍了一种先进的少数拍摄网络入侵检测系统 (NIDS),可以有效地识别使用最小数据的新威胁. 这种新的方法通过提高检测不断变化的网络攻击的适应性和准确性,显著提高了网络安全.
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:
- 传统的网络入侵检测系统 (NIDS) 由于大样本要求而难以适应,特别是在新鲜和罕见的攻击中.
- 现有的短暂的NIDS通常无法捕捉网络流量的远程依赖性,从而限制了它们对复杂威胁的有效性.
研究的目的:
- 提出一种新的高级少量射击网络入侵检测方法.
- 提高NIDS在快速变化的网络环境和复杂的攻击场景中的适应性和准确性.
主要方法:
- 利用自我注意机制来有效地提取特征,并捕获网络流量的远程依赖.
- 嵌入位置编码以保存时间序列并增强时间动态处理.
- 采用了多种更新策略的超级学习,用于初始通用培训和少数镜头微调.
主要成果:
- 在CICIDS2017数据集上实现了99.90%的高检测率,在CICIDS2018数据集上达到98.23%.
- 使用仅10个样本证明了有效性,显著减少了数据依赖.
- 与传统方法相比,表现出更好的适应性和预测准确性.
结论:
- 提议的提升的少数射击NIDS有效地解决了样本稀缺问题,并改善了新型网络入侵的检测.
- 自我关注和代改进提供了一个强大的解决方案,用于增强网络安全,以应对不断变化的网络威胁.
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