Computer Science

Computer Science ›› 2023, Vol. 50 ›› Issue (7): 237-245.doi: 10.11896/jsjkx.220700078

• Artificial Intelligence • Previous Articles     Next Articles

Anomaly Detection Method Based on Context Information Fusion and Noise Adaptation

HENG Hongjun, ZHOU Wenhua   

  1. College of Computer Science and Technology,Civil Aviation University of China,Tianjin 300300,China
  • Received:2022-07-08 Revised:2022-12-08 Online:2023-07-15 Published:2023-07-05
  • About author:HENG Hongjun,born in 1968,Ph.D,associate professor,is a member of China Computer Federation.His main research interests include intelligent information processing,natural language,knowledge graph and anomaly detection.ZHOU Wenhua,born in 1998,postgra-duate.Her main research interests include anomaly detection and multiva-riate time series data.
  • Supported by:
    National Natural Science Foundation of China(U1333109).

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Abstract: The data collected by field devices such as sensors and actuators in cyber-physical systems(CPSs) contains complex context information.To extract and fuse context information in data and reduce the interference caused by noise,an anomaly detection method based on context information fusion and noise adaptation is proposed.In this method,an encoder-decoder network composed of adaptive denoising encoder,context information encoder and decoder is designed to model the state-space model of CPSs.The adaptive denoising encoder generates adaptive noise by fitting the distribution of noise in the data during the training process,and adds the adaptive noise to the sensor data of the training data,so as to improve the robustness of the encoder-decoder network,reduce the interference caused by noise,and force the adaptive denoising decoder to learn the system hidden state with stronger generalization.Context information encoder uses long-short term memory(LSTM) and convolutional neural networks(CNN) to extract temporal context information and spatial context information in the data window,and uses self-attention mechanism to fuse these two types of context information with system hidden state,so as to obtain the fusion result,which is used to infer the system hidden state at the current moment,so as to increase the amount of information of system hidden state at the current moment.The decoder can decode the corresponding sensor data more accurately by using the above system hidden states.After the encoder-decoder network training is completed,the system hidden state and the decoded sensor data are obtained,and the anomaly score is calculated based on the unscented Kalman filter algorithm.Experimental results on two actual CPSs datasets of SWaT and PUMP show that the F1 value of the proposed method is better than other comparison methods,which verifies its effectiveness.

Key words: Anomaly detection, Adaptive noise, Context information, State space model, Cyber-physical systems

CLC Number: 

  • TP183
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