Detection of One Dimensional Anomalies Using a Vector-Based Convolutional Autoencoder

Anomaly detection is important to significant real life entities such as network intrusion and credit card fraud. Existing anomaly detection methods were partially learned the features, which is not appropriate for accurate detection of anomalies. In this study we proposed vector-based convolutional autoencoder (V-CAE) for one dimensional anomaly detection. The core of our model is a linear autoencoder, which is used to construct a low-dimensional manifold of feature vectors for normal data. At the same time, we used vector-based convolutional neural network (V-CNN) to extract the features from vector data before and after the linear autoencoder that makes the model learned deep features for efficient anomaly detection. This unsupervised learning method used only normal data in the training phase. We used the combined abnormal score calculated from two reconstruction errors: (i) error between the input and output of the whole architecture and (ii) error between the input and output of the linear encoder. Compared with the nine state-of-the-arts methods, our proposed V-CAE shows effective and stable results of AUC with 0.996 in estimating anomalies based on several benchmark datasets.