提高网络入侵检测使用集体投票分类器物联网的网络入侵检测
Ashfaq Hussain Farooqi1, Shahzaib Akhtar1, Hameedur Rahman1
1Faculty of Computing and AI, Air University, Islamabad 44000, Pakistan.
Sensors (Basel, Switzerland)
|January 11, 2024
概括
一个新的DRX整体投票分类器提高了6G网络的万物互联网安全性. 这种机器学习方法显著提高了入侵检测的准确性,并减少了多个数据集的错误阳性.
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:
- 6G网络中的万物互联网 (IoE) 扩大了连接,增加了来自尸网络和其他攻击的安全风险.
- 保护支持物联网的元宇宙连接需要强大的安全措施来检测网络异常.
研究的目的:
- 为增强网络入侵检测提出一种新的分类技术.
- 在快速扩展物联网连接的背景下,提高安全系统的准确性和精度.
主要方法:
- 开发了一个DRX组合投票分类器,结合了决策树,随机森林和XGBoost算法.
- 通过使用基准数据集评估拟议的技术:NSL-KDD,UNSW-NB15和CIC-IDS2017.
主要成果:
- 实现了高准确率:99.88% (NSL-KDD),99.93% (UNSW-NB15) 和99.98% (CIC-IDS2017).这些数据的准确率均为99.88% (NSL-KDD),99.93% (UNSW-NB15) 和99.98% (CIC-IDS2017),这些数据的准确率均为99.88%.
- 在数据集中显著降低了假阳性率,达到0.003%,0.001%,0.00012%.
- 与其他现有方法相比,证明了卓越的性能.
结论:
- 基于DRX的集体投票分类器在IoE环境中对网络入侵检测非常有效.
- 拟议的方法为保护6G网络免受新出现的网络威胁提供了强大的解决方案.
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