Accurate online anomaly detection methods are at the core of the development of IoT-related industries,in which online anomaly identification targeting complex and dynamic data streams is one of the important research hotspots. Existing online anomaly detection methods suffer from the problem of processing complexity overload,while offline deep anomaly detection methods suffer from the problem of concept drift due to the change of data distribution. To address the above problems,this paper proposes an online anomaly detection framework with improved adaptive model pooling,which can collaborate with autoencoder-based anomaly detection methods to achieve online anomaly detection. Firstly,the basic anomaly identification is carried out using the autoencoder-based anomaly detection model. Secondly,based on the adaptive model pool,the concept drift detection algorithm is integrated to accurately identify concept drift,adapt to the dynamically changing data flow,and solve the concept drift phenomenon. Finally,the model merging method of the optimised adaptive model pool is optimised,which enhances the capability of online anomaly identification. The experimental results show that compared with the flow variant of autoencoder model and the original adaptive model pool algorithm,the proposed algo-rithm improves the anomaly detection accuracy indexes by 20.2％ and 5.83％ respectively,and meanwhile is higher than the existing online anomaly detection algorithms in the best accuracy indexes by about 16.7％.
关键词
无监督学习/自动编码器/概念漂移/异常检测/自适应模型池/数据流
Key words
unsupervised learning/autoencoder/concept drift/anomaly detection/adaptive model pool/data stream
维普
万方数据