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物流回归集成分类器用于物联网中的入侵检测系统
Silpa Chalichalamala1, Niranjana Govindan2, Ramani Kasarapu3
1Department of Computer Science and Engineering, SRM Institute of Science and Technology, Kattankulathur, Chennai 603203, India.
Sensors (Basel, Switzerland)
|December 9, 2023
概括
本研究介绍了一种用于物联网 (IoT) 的新型入侵检测系统 (IDS). 拟议的基于后勤回归的组合分类器 (LREC) 通过在BoT-IoT数据集上实现99.99%的准确性,显著提高了安全性.
科学领域:
- 计算机科学 计算机科学
- 网络安全 网络安全
- 网络安全 网络安全
背景情况:
- 物联网 (IoT) 在全球范围内连接设备,但引入了安全漏洞.
- 物联网系统中的节点安全漏洞可能导致攻击和异常.
- 有效的入侵检测系统 (IDS) 对于保护物联网技术至关重要.
研究的目的:
- 为物联网 (IoT) 环境提出一个有效的入侵检测系统 (IDS).
- 开发基于物流回归的集成分类器 (LREC) 以提高物联网安全性.
- 解决IDS开发中的数据不平衡和特征选择挑战.
主要方法:
- 提出了一个基于逻辑回归的集群分类器 (LREC),结合了AdaBoost和随机森林 (RF).
- 使用自适应合成采样 (ADASYN) 来处理数据不平衡.
- 在特征选择中使用递归特征消除 (RFE).
- 在BoT-IoT和TON-IoT数据集上评估了RFE-LREC方法.
主要成果:
- 在BoT-IoT数据集上,RFE-LREC实现了99.99%的分类准确性.
- 使用包括准确性,回忆力,精度,F1分数,FAR,ROC,TNR和MCC在内的指标分析了性能.
- 在分类准确性方面超过了TL-IDS和LSTM等现有方法.
结论:
- 拟议的RFE-LREC方法为物联网中的入侵检测系统提供了一个高度有效的解决方案.
- 该方法成功地解决了诸如数据不平衡和特征选择等关键挑战.
- 与现有最先进的方法相比,表现出优越的性能.
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