Evaluation of Machine Learning Algorithms for Anomaly Detection in Industrial Networks

机译：用于工业网络中异常检测的机器学习算法的评估

获取原文

获取原文并翻译 | 示例

页面导航

摘要
著录项
相似文献
相关主题

摘要

The cyber-physical security of Industrial Control Systems (ICSs) represents an actual and worthwhile research topic. In this paper, we compare and evaluate different Machine Learning (ML) algorithms for anomaly detection in industrial control networks. We analyze supervised and unsupervised ML-based anomaly detection approaches using datasets extracted from the Secure Water Treatment (SWaT), a testbed developed to emulate a scaled-down real industrial plant. Our experiments show strengths and limitations of the two ML-based anomaly detection approaches for industrial networks.

机译：工业控制系统（ICSs）的网络物理安全性是一个实际且有价值的研究主题。在本文中，我们比较和评估了用于工业控制网络中异常检测的不同机器学习（ML）算法。我们使用从安全水处理（SWaT）中提取的数据集来分析基于监督和无监督ML的异常检测方法，SWaT是为模拟按比例缩小的实际工厂而开发的测试平台。我们的实验显示了两种基于ML的工业网络异常检测方法的优势和局限性。

著录项

来源
《2019 IEEE International Symposium on Measurements amp; Networking》|2019年|1-6|共6页
会议地点 Catania(IT)
作者
Giuseppe Bernieri; Mauro Conti; Federico Turrin;
展开▼
作者单位

Department of Mathematics, University of Padua, Padua, Italy;

Department of Mathematics, University of Padua, Padua, Italy;

Department of Mathematics, University of Padua, Padua, Italy;

展开▼
会议组织
原文格式 PDF
正文语种 eng
中图分类
关键词
Machine Learning; Anomaly Detection; Industrial Control System; Cyber-Physical System; Security;

机译：机器学习;异常检测;工业控制系统;网络物理系统;安全性;;

相似文献

外文文献
中文文献
专利

1. Anomaly events classification and detection system in critical industrial internet of things infrastructure using machine learning algorithms [J] . Selim Gamal Eldin I., Hemdan E. Z. Z. El-Din, Shehata Ahmed M., Multimedia Tools and Applications . 2021,第8期

机译：机器学习算法基础设施关键工业互联网中的异常事件分类和检测系统
2. Supervised Machine Learning Classification Algorithmic Approach for Finding Anomaly Type of Intrusion Detection in Wireless Sensor Network [J] . Ashwini B. Abhale, S. S. Manivannan Optical memory & neural networks . 2020,第3期

机译：监督机器学习分类算法方法，用于在无线传感器网络中查找异常类型的入侵检测
3. Network Anomaly Detection based on Late Fusion of Several Machine Learning Algorithms [J] . Tran Hoang Hai, Le Huy Hoang, Eui-nam Huh International Journal of Computer Networks & Communications . 2020,第6期

机译：基于几台机器学习算法的晚期融合的网络异常检测
4. Evaluation of Machine Learning Algorithms for Anomaly Detection in Industrial Networks [C] . Giuseppe Bernieri, Mauro Conti, Federico Turrin IEEE International Symposium on Measurements amp;amp;amp;amp;amp;amp; Networking . 2019

机译：工业网络中异常检测机器学习算法的评价
5. Anomaly Detection and Classification of Sparse Gamma-Ray Spectra Using Machine Learning Algorithms for Depleted Uranium Remediation [D] . Finney, Austin Matthew. 2020

机译：使用机器学习算法对耗尽铀修复机器学习算法的异常检测和分类
6. Hierarchical Anomaly Detection Model for In-Vehicle Networks Using Machine Learning Algorithms [O] . Seunghyun Park, Jin-Young Choi 2020

机译：使用机器学习算法的车载网络的分层异常检测模型
7. Anomaly Detection in Medical Wireless Sensor Networks using Machine Learning Algorithms [O] . Pachauri Girik, Sharma Sandeep 2015

机译：使用机器学习算法的医疗无线传感器网络中的异常检测

Evaluation of Machine Learning Algorithms for Anomaly Detection in Industrial Networks

摘要

著录项

相似文献

相关主题

期刊订阅