在工业物联网中使用XGBoost和LSTM进行异常检测的有效方法
Zhen Chen1,2, ZhenWan Li1,2, Jia Huang1,2
1College of Information Science Technology, Hainan Normal University, No. 99 LongKun South Road, Haikou city, 571158, Hainan Province, China.
Scientific reports
|October 13, 2024
概括
本研究介绍了MIX_LSTM,这是一个最佳的深度学习模型,用于工业物联网 (IIoT) 安全. 它通过使用 eXtreme Gradient Boosting来增强异常检测功能选择和优化的损失功能,提高不平衡数据的性能.
科学领域:
- 网络安全 网络安全
- 机器学习 机器学习
- 事物的工业互联网 (IIoT)
背景情况:
- 工业物联网 (IIoT) 系统面临着巨大的安全挑战,因为巨大的数据量和设备数量.
- 现有的异常检测方法在IIoT环境中与不平衡的数据和计算密集型特征选择作斗争.
研究的目的:
- 为IIoT的异常检测提出一个最佳的深度学习模型,解决数据不平衡并减少计算负载.
- 提高IIoT网络中识别异常活动的准确性和效率.
主要方法:
- 使用了极端梯度提升 (XGBoost),具有可变的值,用于有效的特征选择和维度降低.
- 设计和优化了损失函数,以提高不平衡数据集和复杂类别的性能.
- 通过结合最佳特征选择值和优化损失函数,开发了MIX_LSTM模型.
主要成果:
- 在UNSW-NB15和NSL-KDD数据集上,MIX_LSTM模型表现出卓越的性能,实现了高AUC-ROC和AUC-PR值.
- 在UNSW-NB15上实现了0.084的错误报警率 (FAR),在NSL-KDD上达到0.028.
- 在检测IIoT中的异常攻击方面,超过了传统的深度学习和机器学习模型.
结论:
- 拟议的MIX_LSTM模型为IIoT中异常检测提供了有效的解决方案,能够熟练地处理数据不平衡和计算挑战.
- 优化功能选择和损失函数设计的综合方法显著提高了IIoT安全中的异常检测能力.
- MIX_LSTM 提供了一种强大而高效的方法,用于保护智能工业化应对不断变化的网络威胁.
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