Journal of Computer Applications ›› 2023, Vol. 43 ›› Issue (3): 804-811.DOI: 10.11772/j.issn.1001-9081.2022010006
Special Issue: 网络空间安全
• Cyber security • Previous Articles Next Articles
Received:
2022-01-06
Revised:
2022-04-28
Accepted:
2022-04-29
Online:
2022-05-07
Published:
2023-03-10
Contact:
Chunyong YIN
About author:
ZHOU Liwen, born in 1996, M. S. candidate. His researchinterests include anomaly detection, deep learning, big data mining,adversarial attack.
通讯作者:
尹春勇
作者简介:
尹春勇(1977—),男,山东潍坊人,教授,博士生导师,博士,主要研究方向:网络空间安全、大数据挖掘、隐私保护、人工智能、新型计算CLC Number:
Chunyong YIN, Liwen ZHOU. Unsupervised time series anomaly detection model based on re-encoding[J]. Journal of Computer Applications, 2023, 43(3): 804-811.
尹春勇, 周立文. 基于再编码的无监督时间序列异常检测模型[J]. 《计算机应用》唯一官方网站, 2023, 43(3): 804-811.
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URL: https://www.joca.cn/EN/10.11772/j.issn.1001-9081.2022010006
数据集 | 样本总数 | 样本数分类 | 异常比率/% | 特征数 | |
---|---|---|---|---|---|
训练集 | 测试集 | ||||
SWaT | 946 719 | 568 031 | 378 688 | 11.98 | 51 |
WADI | 577 658 | 346 594 | 231 064 | 5.99 | 127 |
SMAP | 562 800 | 337 680 | 225 120 | 13.13 | 25 |
MSL | 132 046 | 79 227 | 52 819 | 10.72 | 55 |
Tab. 1 Statistics of four datasets
数据集 | 样本总数 | 样本数分类 | 异常比率/% | 特征数 | |
---|---|---|---|---|---|
训练集 | 测试集 | ||||
SWaT | 946 719 | 568 031 | 378 688 | 11.98 | 51 |
WADI | 577 658 | 346 594 | 231 064 | 5.99 | 127 |
SMAP | 562 800 | 337 680 | 225 120 | 13.13 | 25 |
MSL | 132 046 | 79 227 | 52 819 | 10.72 | 55 |
模型 | 单变量时间序列 | 多变量时间序列 | 最优 平均F1 | ||
---|---|---|---|---|---|
SMAP | MSL | SWaT | WADI | ||
RTGAN | 0.861 | 0.927 | 0.853 | 0.617 | 0.815 |
Dense-AE | 0.729 | 0.483 | 0.824 | 0.354 | 0.598 |
IF | 0.473 | 0.612 | 0.738 | 0.315 | 0.535 |
USAD | 0.817 | 0.911 | 0.846 | 0.430 | 0.751 |
DAGMM | 0.764 | 0.852 | 0.797 | 0.201 | 0.654 |
LSTM-VAE | 0.684 | 0.579 | 0.804 | 0.380 | 0.612 |
MAD-GAN | 0.381 | 0.124 | 0.810 | 0.624 | 0.485 |
OmniAnomaly | 0.853 | 0.901 | 0.833 | 0.406 | 0.748 |
Tab. 2 Comparison of F1-scores for anomaly detection
模型 | 单变量时间序列 | 多变量时间序列 | 最优 平均F1 | ||
---|---|---|---|---|---|
SMAP | MSL | SWaT | WADI | ||
RTGAN | 0.861 | 0.927 | 0.853 | 0.617 | 0.815 |
Dense-AE | 0.729 | 0.483 | 0.824 | 0.354 | 0.598 |
IF | 0.473 | 0.612 | 0.738 | 0.315 | 0.535 |
USAD | 0.817 | 0.911 | 0.846 | 0.430 | 0.751 |
DAGMM | 0.764 | 0.852 | 0.797 | 0.201 | 0.654 |
LSTM-VAE | 0.684 | 0.579 | 0.804 | 0.380 | 0.612 |
MAD-GAN | 0.381 | 0.124 | 0.810 | 0.624 | 0.485 |
OmniAnomaly | 0.853 | 0.901 | 0.833 | 0.406 | 0.748 |
SNR/% | 异常检测方法 | |||
---|---|---|---|---|
LSTM-VAE | USAD | OmniAnomaly | RTGAN | |
10 | 0.591 | 0.746 | 0.731 | 0.803 |
20 | 0.557 | 0.725 | 0.705 | 0.794 |
30 | 0.497 | 0.695 | 0.711 | 0.789 |
40 | 0.468 | 0.676 | 0.691 | 0.763 |
Tab. 3 Average F1-scores when adding noise at different SNR to original data
SNR/% | 异常检测方法 | |||
---|---|---|---|---|
LSTM-VAE | USAD | OmniAnomaly | RTGAN | |
10 | 0.591 | 0.746 | 0.731 | 0.803 |
20 | 0.557 | 0.725 | 0.705 | 0.794 |
30 | 0.497 | 0.695 | 0.711 | 0.789 |
40 | 0.468 | 0.676 | 0.691 | 0.763 |
1 | CHOI K, YI J, PARK C, et al. Deep learning for anomaly detection in time-series data: review, analysis, and guidelines[J]. IEEE Access, 2021, 9: 120043-120065. 10.1109/ACCESS.2021.3107975 |
2 | YANG J F, SUN Y, LIANG J, et al. Image captioning by incorporating affective concepts learned from both visual and textual components[J]. Neurocomputing, 2019, 328: 56-68. 10.1016/j.neucom.2018.03.078 |
3 | LI X X, KANG Y F, LI F. Forecasting with time series imaging[J]. Expert Systems with Applications, 2020, 160: No.113680. 10.1016/j.eswa.2020.113680 |
4 | LI D, CHEN D C, JIN B H, 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. |
5 | AUDIBERT J, MICHIARDI P, GUYARD F, et al. USAD: unsupervised anomaly detection on multivariate time series[C]// Proceedings of the 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. New York: ACM, 2020: 3395-3404. 10.1145/3394486.3403392 |
6 | FAN C, XIAO F, ZHAO Y, et al. Analytical investigation of autoencoder-based methods for unsupervised anomaly detection in building energy data[J]. Applied Energy, 2018, 211: 1123-1135. 10.1016/j.apenergy.2017.12.005 |
7 | YAO R, LIU C D, ZHANG L X, et al. Unsupervised anomaly detection using variational auto-encoder based feature extraction[C]// Proceedings of the 2019 IEEE International Conference on Prognostics and Health Management. Piscataway: IEEE, 2019: 1-7. 10.1109/icphm.2019.8819434 |
8 | SCHLEGL T, SEEBÖCK P, WALDSTEIN S M, et al. Unsupervised anomaly detection with generative adversarial networks to guide marker discovery[C]// Proceedings of the 2017 International Conference on Information Processing in Medical Imaging, LNCS 10265. Cham: Springer, 2017: 146-157. |
9 | DENDORFER P, ELFLEIN S, LEAL-TAIXÉ L. MG-GAN: a multi-generator model preventing out-of-distribution samples in pedestrian trajectory prediction[C]// Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision. Piscataway: IEEE, 2021: 13138-13147. 10.1109/iccv48922.2021.01291 |
10 | KIEU T, YANG B, JENSEN C S. Outlier detection for multidimensional time series using deep neural networks[C]// Proceedings of the 19th IEEE International Conference on Mobile Data Management. Piscataway: IEEE, 2018: 125-134. 10.1109/mdm.2018.00029 |
11 | REN H S, XU B X, WANG Y J, et al. Time-series anomaly detection service at Microsoft[C]// Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM, 2019: 3009-3017. 10.1145/3292500.3330680 |
12 | COOK A A, MISIRLI G, FAN Z. Anomaly detection for IoT time-series data: a survey[J]. IEEE Internet of Things Journal, 2020, 7(7): 6481-6494. 10.1109/jiot.2019.2958185 |
13 | RAMASWAMY S, RASTOGI R, SHIM K, et al. Efficient algorithms for mining outliers from large data sets[C]// Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data. New York: ACM, 2000: 427-438. 10.1145/335191.335437 |
14 | BREUNIG M M, KRIEGEL H P, NG R T, et al. LOF: identifying density-based local outliers[C]// Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data. New York: ACM, 2000: 93-104. 10.1145/335191.335388 |
15 | ZARE MOAYEDI H, MASNADI-SHIRAZI M A. ARIMA model for network traffic prediction and anomaly detection[C]// Proceedings of the 2008 International Symposium on Information Technology. Piscataway: IEEE, 2008: 1-6. 10.1109/itsim.2008.4631947 |
16 | HE Q P, QIN S J, WANG J. A new fault diagnosis method using fault directions in Fisher discriminant analysis[J]. AIChE Journal, 2005, 51(2): 555-571. 10.1002/aic.10325 |
17 | AHMAD S, LAVIN A, PURDY S, et al. Unsupervised real-time anomaly detection for streaming data[J]. Neurocomputing, 2017, 262: 134-147. 10.1016/j.neucom.2017.04.070 |
18 | RINGBERG H, SOULE A, REXFORD J, et al. Sensitivity of PCA for traffic anomaly detection[C]// Proceedings of the 2017 ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems. New York: ACM, 2007: 109-120. 10.1145/1254882.1254895 |
19 | DAI L, LIN T, LIU C, et al. SDFVAE: static and dynamic factorized VAE for anomaly detection of multivariate CDN KPIs[C]// Proceedings of the 2021 World Wide Web Conference. New York: ACM, 2021: 3076-3086. 10.1145/3442381.3450013 |
20 | 霍纬纲,王慧芳. 基于自编码器和隐马尔可夫模型的时间序列异常检测方法[J]. 计算机应用, 2020, 40(5): 1329-1334. |
HUO W G, WANG H F. Time series anomaly detection method based on autoencoder and HMM[J]. Journal of Computer Applications, 2020, 40(5): 1329-1334. | |
21 | VON SCHLEINITZ J, GRAF M, TRUTSCHNIG W, et al. VASP: an autoencoder-based approach for multivariate anomaly detection and robust time series prediction with application in motorsport[J]. Engineering Applications of Artificial Intelligence, 2021, 104: No.104354. 10.1016/j.engappai.2021.104354 |
22 | GOODFELLOW I J, POUGET-ABADIE J, MIRZA M, et al. Generative adversarial nets[C]// Proceedings of the 27th International Conference on Neural Information Processing Systems - Volume 2. Cambridge: MIT Press, 2014: 2672-2680. |
23 | YOOH J, JARRETT D, M VAN DER SCHAAR. Time-series generative adversarial networks[C/OL]// Proceedings of the 33rd Conference on Neural Information Processing Systems. [2021-09-21].. |
24 | 王静,邹慧敏,曲东东,等. 基于经验模态分解生成对抗网络的金融时间序列预测[J]. 计算机应用与软件, 2020, 37(5): 293-297. 10.3969/j.issn.1000-386x.2020.05.050 |
WANG J, ZOU H M, QU D D, et al. Financial time series prediction based on empirical mode decomposition to generate adversarial networks[J]. Computer Applications and Software, 2020, 37(5): 293-297. 10.3969/j.issn.1000-386x.2020.05.050 | |
25 | GULRAJANI I, AHEMD F, ARJOVSKY M, et al. Improved training of Wasserstein GANs[C]// Proceedings of the 30th International Conference on Neural Information Processing Systems. Red Hook, NY: Curran Associates Inc., 2017: 5769-5779. |
26 | SU Y, ZHAO Y J, NIU C H, 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. |
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