摘要
针对传统半监督深度异常检测模型对非平衡多维数据分布学习能力不足及模型训练困难等问题,提出一种基于VAE-WGAN架构的多维时间序列异常检测方法,利用VAE作为WGAN的生成器,使用Wasserstein距离作为模型拟合分布与待测数据真实分布之间的度量,学习复杂的高维数据分布。利用滑动窗口划分时间序列,使用正常序列数据训练模型;根据待测序列在训练好的模型中的异常得分,结合自适应阈值技术进行异常判定。实验表明,该方法具有模型容易训练且稳定性强的特点,并且在精确率、召回率､F1值等异常检测性能指标上,比现有的生成式异常检测模型有明显提升。
As the deficiency of learning ability of traditional semi-supervised depth anomaly detection model to unbalanced multidimensional data distribution and the difficulty of model training,a multi-dimensional time series anomaly detection method based on VAE-WGAN architecture was proposed.VAE was used as a generator of WGAN.The Wasserstein distance was used as a measure between the model fitting distribution and the real distri-bution of the data to be measured,complex and high-dimensional data distributions could be learned.A sliding window was applied to divide the time series,the normal sequence data were used to train the model.According to the abnormal score of the waiting test sequence in the trained model,the anomaly was judged with adaptive thre-shold technology.The experimental results show that the model is easy to train and stable,and has obvious im-provement over the existing generative anomaly detection model in accuracy,recall rate,F1 score and other ano-maly detection performance indicators.
作者
段雪源
付钰
王坤
DUAN Xueyuan;FU Yu;WANG Kun(Department of Information Security,Naval University of Engineering,Wuhan 430033,China;College of Computer and Information Technology,Xinyang Normal University,Xinyang 464000,China;School of Mathematics and Information Engineering,Xinyang Vocational and Technical College,Xinyang 464000,China)
出处
《通信学报》
EI
CSCD
北大核心
2022年第3期1-13,共13页
Journal on Communications
基金
国家重点研发计划基金资助项目(No.2018YFB0804104)。
