Survey on Machine Learning-Based Anomaly Detection for Industrial Internet

被引：0
|
作者：
Liu Q. [1 ,2 ]
Chen Y. [1 ,2 ]
Ni J. [1 ,2 ]
Luo C. [3 ]
Liu C. [4 ]
Cao Y. [1 ]
Tan R. [1 ]
Feng Y. [1 ]
Zhang Y. [1 ,2 ]
机构：
[1] Institute of Information Engineering, Chinese Academy of Sciences, Beijing
[2] School of Cyber Security, University of Chinese Academy of Sciences, Beijing
[3] China Academy of Information and Communications Technology, Beijing
[4] China Industrial Control Systems Cyber Emergency Response Team, Beijing
来源：
Jisuanji Yanjiu yu Fazhan/Computer Research and Development | 2022年 / 59卷 / 05期
基金：
中国国家自然科学基金;
关键词：
Industrial control system (ICS); Industrial Internet; Intrusion detection; Machine learning; Taxonomy;
D O I：
10.7544/issn1000-1239.20211147
中图分类号：
学科分类号：
摘要：
Machine learning has achieved great success in computer vision, natural language processing and other fields in the past few years. In recent years, machine learning technology has gradually become one of the mainstream technologies in the field of cyber-security, and many intrusion detection technologies based on machine learning have emerged in the field of the industrial Internet. Aiming at landing machine learning-based intrusion detection technology into the real industrial system network, we conduct an in-depth analysis of related work in the field. We summarize the uniqueness of machine learning-based intrusion detection in the industrial Internet and extract three research points from the workflow of intrusion detection in industrial control system (ICS). Based on the research points that different researches focus on, we divide machine learning-based intrusion detection system (IDS) in ICS into three categories: algorithm design-oriented researches, application challenges and limitations-oriented researches, and ICS attack scenario-oriented researches. The taxonomy shows the significance of different research work as well as exposes the problems existing in the research field at present. It can provide a good research direction and reference for future work. In the end, we propose two promising research directions in this field based on the latest developments in machine learning. © 2022, Science Press. All right reserved.
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页码：994 / 1014
页数：20
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共 107 条
  • [1] Lecun Y, Bottou L, Bengio Y, Et al., Gradient-based learning applied to document recognition, Proceedings of the IEEE, 86, 11, pp. 2278-2324, (1998)
  • [2] Simonyan K, Zisserman A., Very deep convolutional networks for large-scale image recognition, (2014)
  • [3] Szegedy C, Liu Wei, Jia Yangqing, Et al., Going deeper with convolutions, Proc of 2015 IEEE Conf on Computer Vision and Pattern Recognition (CVPR), pp. 1-9, (2015)
  • [4] Ioffe S, Szegedy C., Batch normalization: Accelerating deep network training by reducing internal covariate shift, Proc of the 32nd Int Conf on Machine Learning(ICML'15), pp. 448-456, (2015)
  • [5] He Kaiming, Zhang Xiangyu, Ren Shaoqing, Et al., Deep residual learning for image recognition, Proc of 2016 IEEE Conf on Computer Vision and Pattern Recognition (CVPR), pp. 770-778, (2016)
  • [6] Socher R, Pennington J, Huang E H, Et al., Semi-supervised recursive autoencoders for predicting sentiment distributions, Proc of the 2011 Conf on Empirical Methods in Natural Language Processing, pp. 151-161, (2011)
  • [7] Mueller J, Thyagarajan A., Siamese recurrent architectures for learning sentence similarity, Proc of the 30th AAAI Conf on Artificial Intelligence(AAAI'16), pp. 2786-2792, (2016)
  • [8] Peng Hao, Li Jianxin, He Yu, Et al., Large-scale hierarchical text classification with recursively regularized deep graph-CNN, Proc of the 2018 World Wide Web Conf (WWW'18), pp. 1063-1072, (2018)
  • [9] Turc I, Chang M W, Lee K, Et al., Well-read students learn better: On the importance of pre-training compact models, (2019)
  • [10] Ma Xuezhe, Hovy E., End-to-end sequence labeling via bi-directional LSTM-CNNs-CRF, Proc of the 54th Annual Meeting of the Association for Computational Linguistics, pp. 1064-1074, (2016)
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