优化了物联网和雾计算中的入侵检测,使用集体学习和高级功能选择
1Faculty of Computer and Information Technology, Sana'a University, Sana'a, Yemen.
PloS one
|August 1, 2024
概括
本研究引入了一种用于物联网 (IoT) 和雾计算环境中的入侵检测的新框架. 该系统在识别网络威胁方面达到99%以上的准确性,提高了网络安全.
科学领域:
- 网络安全 网络安全
- 网络安全 网络安全
- 应用机器学习应用机器学习
背景情况:
- 物联网 (IoT) 和雾计算引入了重要的安全漏洞.
- 传统的入侵检测系统面临由于雾环境中的资源限制而受到限制.
- 有效的异常检测对于确保现代网络基础设施的安全至关重要.
研究的目的:
- 为雾和物联网网络提出一种新,高效和准确的入侵检测框架.
- 为应对雾节点有限的计算资源所带来的挑战.
- 在分布式网络架构中增强对不断发展的网络攻击的检测.
主要方法:
- 集成堆叠的自动编码器用于特征提取和维度减少.
- 利用CatBoost进行功能改进和预测选择.
- 开发一个优化的组合模型,结合变压器-CNN-LSTM进行全面的流量分析.
- 边缘预处理和基于云的集体学习管道的实施.
主要成果:
- 在多个基准数据集 (NSL-KDD,UNSW-NB15,AWID) 中实现了超过99%的威胁检测准确度.
- 通过集成边缘和云处理证明了高效和准确的异常检测.
- 验证了框架在传统,混合和无线环境中的有效性.
结论:
- 拟议的框架提供了一种可行的解决方案,用于保护雾和物联网基础设施免受复杂的网络攻击.
- 混合方法有效地平衡边缘和云资源之间的计算负载.
- 该系统提供强大的保护,防止不断变化的网络威胁.
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