CNN and LSTM based Encoder-Decoder for Anomaly Detection in Multivariate Time Series

The purpose of anomaly detection is to detect data that deviates from the expected, and is widely used in intrusion detection, data preprocessing and so on.For data anomaly detection, we propose a data anomaly detection algorithm based on convolutional neural network and Encoder-Decoder architecture CNN-LSTMED (Convolutional Neural Networks Long Short-Term Encoder-Decoder).First,we use the convolutional neural network to encode the time series data to obtain the encoded sequence,and use the features extracted from the sequence as the input of the nonlinear model long short-term memory network LSTM (Long Short-Term Memory) to decode and output the decoded sequence. Finally, the reconstruction error is calculated and the threshold is set to determine the abnormal point. Through experimental comparison with GRUED (Gated Recurrent Neural Encoder-Decoder), LSTMED (Long Short-Term Memory Encoder-Decoder) ,and other algorithms on the KDD99 data set and credit card fraud data set,it turns out that our algorithm has strong robustness and accuracy .