Journal of Computer Applications ›› 2023, Vol. 43 ›› Issue (3): 804-811.DOI: 10.11772/j.issn.1001-9081.2022010006
• 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: http://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 |
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