基于VAE与局部上下文信息自提取的异常检测模型
Computer Engineering and Design(2023)
Abstract
为提升多维时序数据异常检测的准确率,考虑到异常类型的多样性,提出一种基于变分自编码器(variational au-toencoder,VAE)与局部上下文信息自提取的异常检测模型AusVAE-CL(anomaly union score computed by VAE with CNN and LSTM).利用CNN(convolutional neural networks)提取每个时间点的局部上下文信息处理上下文异常;使用LSTM(long short-term memory)作为VAE的前馈神经网络捕获多维时序数据中的时间依赖信息处理集体异常;通过全连接层融合时间依赖信息和局部上下文信息拟合VAE的近似后验分布对正常模式下的系统行为建模,提升正样本表示学习的质量;引入重构误差与相对熵加权和的异常评分方法判定异常.实验结果表明,AusVAE-CL模型的召回率和F1值较经典时序异常检测方法均有所提升.
MoreKey words
anomaly detection,multivariate time series,variational autoencoder,convolutional neural network,long short-term memory network,context anomalies,collective anomalies
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