Abstract
Detecting anomalies in multivariate time series(MTS) data plays an important role in many domains. The abnormal values could indicate events, medical abnormalities,cyber-attacks, or faulty devices which if left undetected could lead to significant loss of resources, capital, or human lives. In this paper, we propose a novel and innovative approach to anomaly detection called Bayesian State-Space Anomaly Detection(BSSAD). The BSSAD consists of two modules: the neural network module and the Bayesian state-space module. The design of our approach combines the strength of Bayesian state-space algorithms in predicting the next state and the effectiveness of recurrent neural networks and autoencoders in understanding the relationship between the data to achieve high accuracy in detecting anomalies. The modular design of our approach allows flexibility in implementation with the option of changing the parameters of the Bayesian state-space models or swap-ping neural network algorithms to achieve different levels of performance. In particular, we focus on using Bayesian state-space models of particle filters and ensemble Kalman filters. We conducted extensive experiments on five different datasets. The experimental results show the superior performance of our model over baselines, achieving an F1-score greater than 0.95. In addition, we also propose using a metric called MatthewCorrelation Coefficient (MCC) to obtain more comprehensive information about the accuracy of anomaly detection.
Abstract (translated)
在多变量时间序列数据中检测异常扮演着在许多领域中重要角色。异常值可能表明事件、医学异常情况、网络攻击或故障设备,如果未被发现,可能会导致严重的资源、资本或人类损失。在本文中,我们提出了一种新颖、创新的方法来检测异常,它被称为贝叶斯空间域异常检测(BSSAD)。BSSAD由两个模块组成:神经网络模块和贝叶斯空间域模块。我们的设计结合了贝叶斯空间域算法在预测下一个状态的强项和循环神经网络和自编码器在理解数据之间的关系的有效性,以在检测异常方面实现高精度。我们的方法模块化设计允许灵活地实现,可以选择改变贝叶斯空间域模型的参数或交换神经网络算法以实现不同的性能水平。特别地,我们专注于使用粒子滤波和集成群体kalman滤波的贝叶斯空间域模型。我们研究了五个不同数据集,实验结果显示,我们的模型相对于基准模型表现出更好的性能,F1得分超过0.95。此外,我们还提出了使用称为Matthew correlation Coefficient(MCC)的度量来获取更全面了解异常检测精度的方法。
URL
https://arxiv.org/abs/2301.13031
