Journal of Computer Applications ›› 2023, Vol. 43 ›› Issue (12): 3799-3805.DOI: 10.11772/j.issn.1001-9081.2022111796
• Cyber security • Previous Articles Next Articles
Anqin ZHANG1,2, Xiaohui WANG1()
Received:
2022-12-06
Revised:
2023-03-16
Accepted:
2023-03-23
Online:
2023-04-10
Published:
2023-12-10
Contact:
Xiaohui WANG
About author:
ZHANG Anqin, born in 1974, Ph. D., associate professor. Her research interests include data mining, ubiquitous computing.
Supported by:
通讯作者:
王小慧
作者简介:
张安勤(1974—),女,安徽六安人,副教授,博士,主要研究方向:数据挖掘、普适计算;
基金资助:
CLC Number:
Anqin ZHANG, Xiaohui WANG. Power battery safety warning based on time series anomaly detection[J]. Journal of Computer Applications, 2023, 43(12): 3799-3805.
张安勤, 王小慧. 基于时序异常检测的动力电池安全预警[J]. 《计算机应用》唯一官方网站, 2023, 43(12): 3799-3805.
Add to citation manager EndNote|Ris|BibTeX
URL: http://www.joca.cn/EN/10.11772/j.issn.1001-9081.2022111796
数据集 | 训练集样本数 | 测试集样本数 | 维度 | 异常率/% |
---|---|---|---|---|
SWaT | 496 800 | 449 919 | 51 | 11.98 |
SMAP | 135 183 | 427 617 | 55 | 10.72 |
MSL | 58 317 | 73 729 | 25 | 13.13 |
EV | 343 626 | 33 811 | 22 | 9.17 |
Tab.1 Statistics of datasets
数据集 | 训练集样本数 | 测试集样本数 | 维度 | 异常率/% |
---|---|---|---|---|
SWaT | 496 800 | 449 919 | 51 | 11.98 |
SMAP | 135 183 | 427 617 | 55 | 10.72 |
MSL | 58 317 | 73 729 | 25 | 13.13 |
EV | 343 626 | 33 811 | 22 | 9.17 |
模型 | SWaT | EV | SMAP | MSL | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
P | R | F1 | P | R | F1 | P | R | F1 | P | R | F1 | |
MAD-GAN | 0.913 2 | 0.689 2 | 0.785 5 | 0.324 5 | 0.753 3 | 0.453 6 | 0.752 8 | 0.894 1 | 0.817 4 | 0.852 2 | 1.000 0 | 0.920 2 |
OmniAnomaly | 0.868 3 | 0.832 3 | 0.854 5 | 0.892 2 | ||||||||
USAD | 0.996 1 | 0.702 1 | 0.823 7 | 0.319 9 | 0.461 4 | 0.862 5 | 1.000 0 | 0.926 2 | 0.856 4 | 1.000 0 | 0.922 6 | |
GDN | 0.681 2 | 0.808 2 | 0.478 3 | 0.258 9 | 0.336 0 | 0.876 2 | 0.931 4 | 0.841 8 | 1.000 0 | 0.914 1 | ||
TranAD | 0.976 0 | 0.699 7 | 0.815 1 | 0.445 9 | 0.316 3 | 0.370 1 | 0.849 7 | 1.000 0 | 0.918 7 | 0.951 4 | ||
CT-ED | 0.846 7 | 0.941 2 | 0.891 4 | 0.712 9 | 0.775 2 | 0.742 8 | 0.911 3 | 0.950 9 | 1.000 0 | 0.951 1 |
Tab.2 Experimental results of CT-ED on different datasets
模型 | SWaT | EV | SMAP | MSL | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
P | R | F1 | P | R | F1 | P | R | F1 | P | R | F1 | |
MAD-GAN | 0.913 2 | 0.689 2 | 0.785 5 | 0.324 5 | 0.753 3 | 0.453 6 | 0.752 8 | 0.894 1 | 0.817 4 | 0.852 2 | 1.000 0 | 0.920 2 |
OmniAnomaly | 0.868 3 | 0.832 3 | 0.854 5 | 0.892 2 | ||||||||
USAD | 0.996 1 | 0.702 1 | 0.823 7 | 0.319 9 | 0.461 4 | 0.862 5 | 1.000 0 | 0.926 2 | 0.856 4 | 1.000 0 | 0.922 6 | |
GDN | 0.681 2 | 0.808 2 | 0.478 3 | 0.258 9 | 0.336 0 | 0.876 2 | 0.931 4 | 0.841 8 | 1.000 0 | 0.914 1 | ||
TranAD | 0.976 0 | 0.699 7 | 0.815 1 | 0.445 9 | 0.316 3 | 0.370 1 | 0.849 7 | 1.000 0 | 0.918 7 | 0.951 4 | ||
CT-ED | 0.846 7 | 0.941 2 | 0.891 4 | 0.712 9 | 0.775 2 | 0.742 8 | 0.911 3 | 0.950 9 | 1.000 0 | 0.951 1 |
方法 | F1 | P | R |
---|---|---|---|
POT | 0.733 5 | 0.705 1 | 0.764 3 |
均方 | 0.525 1 | 0.381 7 | 0.841 0 |
EWMA | 0.127 6 | 0.091 7 | 0.209 6 |
NDT | 0.754 8 | 0.838 2 | 0.686 6 |
POT+剪枝 | 0.741 5 | 0.721 5 | 0.762 7 |
Tab.3 Experimental results of same model with different threshold selection methods
方法 | F1 | P | R |
---|---|---|---|
POT | 0.733 5 | 0.705 1 | 0.764 3 |
均方 | 0.525 1 | 0.381 7 | 0.841 0 |
EWMA | 0.127 6 | 0.091 7 | 0.209 6 |
NDT | 0.754 8 | 0.838 2 | 0.686 6 |
POT+剪枝 | 0.741 5 | 0.721 5 | 0.762 7 |
变量类型 | 变量 | F1 | P | R |
---|---|---|---|---|
多元变量 | 多元变量 | 0.742 8 | 0.712 9 | 0.775 2 |
单变量 | 电压 | 0.572 4 | 0.902 2 | 0.419 2 |
电流 | 0.695 4 | 0.723 3 | 0.669 5 | |
绝缘电阻 | 0.168 2 | 0.091 8 | 1.000 0 |
Tab.4 Variate experimental results of CT-ED
变量类型 | 变量 | F1 | P | R |
---|---|---|---|---|
多元变量 | 多元变量 | 0.742 8 | 0.712 9 | 0.775 2 |
单变量 | 电压 | 0.572 4 | 0.902 2 | 0.419 2 |
电流 | 0.695 4 | 0.723 3 | 0.669 5 | |
绝缘电阻 | 0.168 2 | 0.091 8 | 1.000 0 |
模型 | F1 | P | R |
---|---|---|---|
TranAD | 0.746 9 | 0.916 9 | 0.630 1 |
USAD | 0.672 2 | 0.550 6 | 0.862 6 |
MAD-GAN | 0.401 2 | 0.952 8 | 0.254 1 |
OmniAnomaly | 0.756 9 | 0.928 7 | 0.638 8 |
Tab.5 Experimental results of different models on current univariate time series
模型 | F1 | P | R |
---|---|---|---|
TranAD | 0.746 9 | 0.916 9 | 0.630 1 |
USAD | 0.672 2 | 0.550 6 | 0.862 6 |
MAD-GAN | 0.401 2 | 0.952 8 | 0.254 1 |
OmniAnomaly | 0.756 9 | 0.928 7 | 0.638 8 |
模型 | P | R | F1 |
---|---|---|---|
CT-ED | 0.712 9 | 0.775 2 | 0.742 8 |
CT-ED-WO-Conv | 0.616 1 | 0.808 4 | 0.699 3 |
CT-ED-WO-Feature | 0.667 8 | 0.765 6 | 0.713 3 |
CT-ED-WO-Time | 0.586 7 | 0.762 7 | 0.663 2 |
CT-ED-WO-Contrastive | 0.636 3 | 0.766 2 | 0.695 2 |
Tab. 6 Results of ablation experiment of CT-ED on EV dataset
模型 | P | R | F1 |
---|---|---|---|
CT-ED | 0.712 9 | 0.775 2 | 0.742 8 |
CT-ED-WO-Conv | 0.616 1 | 0.808 4 | 0.699 3 |
CT-ED-WO-Feature | 0.667 8 | 0.765 6 | 0.713 3 |
CT-ED-WO-Time | 0.586 7 | 0.762 7 | 0.663 2 |
CT-ED-WO-Contrastive | 0.636 3 | 0.766 2 | 0.695 2 |
数据增强方式 | F1 | P | R |
---|---|---|---|
FFT | 0.777 7 | 0.823 7 | 0.736 5 |
移动窗口 | 0.773 0 | 0.811 6 | 0.737 8 |
噪声 | 0.774 3 | 0.815 3 | 0.737 2 |
Tab. 7 Results comparison of different data augmentation methods
数据增强方式 | F1 | P | R |
---|---|---|---|
FFT | 0.777 7 | 0.823 7 | 0.736 5 |
移动窗口 | 0.773 0 | 0.811 6 | 0.737 8 |
噪声 | 0.774 3 | 0.815 3 | 0.737 2 |
1 | 王震坡,李晓宇,袁昌贵,等. 大数据下电动汽车动力电池故障诊断技术挑战与发展趋势[J]. 机械工程学报, 2021, 57(14):52-63. 10.3901/jme.2021.14.052 |
WANG Z P, LI X Y, YUAN G C, et al. Challenge and prospects for fault diagnosis of power battery system for electrical vehicles based on big-data[J]. Journal of Mechanical Engineering, 2021, 57(14):52-63. 10.3901/jme.2021.14.052 | |
2 | TULI S, CASALE G, JENNINGS N R. TranAD: deep transformer networks for anomaly detection in multivariate time series data[J]. Proceedings of the VLDB Endowment, 2022, 5(16):1201-1214. |
3 | 毛文涛,施华东,张艳娜,等. 轴承在线早期故障检测的无监督张量深度迁移学习方法[J/OL]. 控制与决策 (2022-11-27) [2023-03-04].. |
MAO W T, SHI H D, ZHANG Y N, et al. Research on unsupervised tensor-based deep transfer learning for online early fault detection of bearing[J/OL]. Control and Decision (2022-11-27) [2023-03-04].. | |
4 | HUNDMAN K, CONSTANTINOU V, LAPORTE C, et al. Detecting spacecraft anomalies using LSTMs and nonparametric dynamic thresholding [C]// Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM, 2018: 387-395. 10.1145/3219819.3219845 |
5 | ZHOU H, YU K, ZHANG X, et al. Contrastive autoencoder for anomaly detection in multivariate time series[J]. Information Sciences, 2022, 610: 266-280. 10.1016/j.ins.2022.07.179 |
6 | VASWANI A, SHAZEER N, PARMAR N, et al. Attention is all you need [C]// Proceedings of the 31st International Conference on Neural Information Processing Systems. Red Hook, NY: Curran Associates Inc., 2017: 6000-6010. |
7 | CHEN T, KORNBLITH S, NOROUZI M, et al. A simple framework for contrastive learning of visual representations[C]// Proceedings of the 37th International Conference on Machine Learning. New York: JMLR.org, 2020: 1597-1607. |
8 | HU J, SHEN L, SUN G. Squeeze-and-excitation networks[C]// Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2018: 7132-7141. 10.1109/cvpr.2018.00745 |
9 | LIN S, CLARK R, BIRKE R, et al. Anomaly detection for time series using VAE-LSTM hybrid model [C]// Proceedings of the 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing. Piscataway: IEEE, 2020: 4322-4326. 10.1109/icassp40776.2020.9053558 |
10 | XU H, CHEN W, ZHAO N, et al. Unsupervised anomaly detection via variational auto-encoder for seasonal KPIs in Web applications[C]// Proceedings of the 2018 World Wide Web Conference. Republic and Canton of Geneva: International World Wide Web Conferences Steering Committee, 2018: 187-196. 10.1145/3178876.3185996 |
11 | PARK D, HOSHI Y, KEMP C C. A multimodal anomaly detector for robot-assisted feeding using an LSTM-based variational autoencoder[J]. IEEE Robotics and Automation Letters, 2018, 3(3): 1544-1551. 10.1109/lra.2018.2801475 |
12 | LI D, CHEN D, JIN B, et al. MAD-GAN: multivariate anomaly detection for time series data with generative adversarial networks[C]// Proceedings of the 2019 International Conference on Artificial Neural Networks, LNCS 11730. Cham: Springer, 2019: 703-716. |
13 | SU Y, ZHAO Y, NIU C, et al. Robust anomaly detection for multivariate time series through stochastic recurrent neural network[C]// Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM, 2019: 2828-2837. 10.1145/3292500.3330672 |
14 | ZHANG C, SONG D, CHEN Y, et al. A deep neural network for unsupervised anomaly detection and diagnosis in multivariate time series data[C]// Proceedings of the 33rd AAAI Conference on Artificial Intelligence. Palo Alto, CA: AAAI Press, 2019: 1409-1416. 10.1609/aaai.v33i01.33011409 |
15 | DENG A, HOOI B. Graph neural network-based anomaly detection in multivariate time series [C]// Proceedings of the 35th AAAI Conference on Artificial Intelligence. Palo Alto, CA: AAAI Press, 2021: 44027-4035. 10.1609/aaai.v35i5.16523 |
16 | ZHAO H, WANG Y, DUAN J, et al. Multivariate time-series anomaly detection via graph attention network [C]// Proceedings of the 2020 IEEE International Conference on Data Mining. Piscataway: IEEE, 2020: 841-850. 10.1109/icdm50108.2020.00093 |
17 | AUDIBERT J, MICHIARDI P, GUYARD F, et al. USAD: unsupervised anomaly detection on multivariate time series[C]// Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM, 2020: 3395-3404. 10.1145/3394486.3403392 |
18 | WANG X, PI D, ZHANG X, et al. Variational Transformer-based anomaly detection approach for multivariate time series[J]. Measurement, 2022, 191: No.110791. 10.1016/j.measurement.2022.110791 |
19 | ZHOU H, ZHANG S, PENG J, et al. Informer: beyond efficient transformer for long sequence time-series forecasting[C]// Proceedings of the 35th AAAI Conference on Artificial Intelligence. Palo Alto, CA: AAAI Press, 2021: 11106-11115. 10.1609/aaai.v35i12.17325 |
20 | SIFFER A, FOUQUE P A, TERMIER A, et al. Anomaly detection in streams with extreme value theory [C]// Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM, 2017: 1067-1075. 10.1145/3097983.3098144 |
21 | MATHUR A P, TIPPENHAUER N O. SWaT: a water treatment testbed for research and training on ICS security [C]// Proceedings of the 2016 International Workshop on Cyber-Physical Systems for Smart Water Networks. Piscataway: IEEE, 2016: 31-36. 10.1109/cyswater.2016.7469060 |
22 | AHMED C M, ZHOU J, MATHUR A P. Noise matters: using sensor and process noise fingerprint to detect stealthy cyber attacks and authenticate sensors in CPS[C]// Proceedings of the 34th Annual Computer Security Applications Conference. New York: ACM, 2018: 566-581. 10.1145/3274694.3274748 |
[1] | Li XU, Xiangyuan FU, Haoran LI. Spatial-temporal traffic flow prediction model based on gated convolution [J]. Journal of Computer Applications, 2023, 43(9): 2760-2765. |
[2] | Hui LU, Ruizhang HUANG, Jingjing XUE, Lina REN, Chuan LIN. DDDC: deep dynamic document clustering model [J]. Journal of Computer Applications, 2023, 43(8): 2370-2375. |
[3] | Chaoshuai QI, Wensi HE, Yi JIAO, Yinghong MA, Wei CAI, Suping REN. Survey on anomaly detection algorithms for unmanned aerial vehicle flight data [J]. Journal of Computer Applications, 2023, 43(6): 1833-1841. |
[4] | Lianpeng QIU, Chengyun SONG. Noise robust dynamic time warping algorithm [J]. Journal of Computer Applications, 2023, 43(6): 1855-1860. |
[5] | Jingsheng LEI, Kaijun LA, Shengying YANG, Yi WU. Joint entity and relation extraction based on contextual semantic enhancement [J]. Journal of Computer Applications, 2023, 43(5): 1438-1444. |
[6] | Kai ZHANG, Zhengchu QIN, Yue LIU, Xinyi QIN. Multi-learning behavior collaborated knowledge tracing model [J]. Journal of Computer Applications, 2023, 43(5): 1422-1429. |
[7] | Zhe XU, Zhihong WANG, Cunyu SHAN, Yaru SUN, Ying YANG. Unsupervised face forgery video detection based on reconstruction error [J]. Journal of Computer Applications, 2023, 43(5): 1571-1577. |
[8] | Jin XIA, Zhengqun WANG, Shiming ZHU. Traffic flow prediction model based on time series decomposition [J]. Journal of Computer Applications, 2023, 43(4): 1129-1135. |
[9] | Rong GAO, Jiawei SHEN, Xiongkai SHAO, Xinyun WU. Instance segmentation algorithm based on Fastformer and self-supervised contrastive learning [J]. Journal of Computer Applications, 2023, 43(4): 1062-1070. |
[10] | Rongjun CHEN, Xuanhui YAN, Chaocheng YANG. Fusion imaging-based recurrent capsule classification network for time series [J]. Journal of Computer Applications, 2023, 43(3): 692-699. |
[11] | Chunyong YIN, Liwen ZHOU. Unsupervised time series anomaly detection model based on re-encoding [J]. Journal of Computer Applications, 2023, 43(3): 804-811. |
[12] | Li YANG, Jianting CHEN, Yang XIANG. Performance optimization strategy of distributed storage for industrial time series big data based on HBase [J]. Journal of Computer Applications, 2023, 43(3): 759-766. |
[13] | Shaokang XU, Zhancheng ZHANG, Haonan YAO, Zhiwei ZOU, Baocheng ZHANG. 2D/3D spine medical image real-time registration method based on pose encoder [J]. Journal of Computer Applications, 2023, 43(2): 589-594. |
[14] | Zhifeng MA, Junyang YU, Longge WANG. Diversity represented deep subspace clustering algorithm [J]. Journal of Computer Applications, 2023, 43(2): 407-412. |
[15] | Yaling XUN, Linqing WANG, Jianghui CAI, Haifeng YANG. Partial periodic pattern incremental mining of time series data based on multi-scale [J]. Journal of Computer Applications, 2023, 43(2): 391-397. |
Viewed | ||||||
Full text |
|
|||||
Abstract |
|
|||||