  Discrete event sequences are ubiquitous, such as an ordered event series of
process interactions in Information and Communication Technology systems.
Recent years have witnessed increasing efforts in detecting anomalies with
discrete-event sequences. However, it still remains an extremely difficult task
due to several intrinsic challenges including data imbalance issues, the
discrete property of the events, and sequential nature of the data. To address
these challenges, in this paper, we propose OC4Seq, a multi-scale one-class
recurrent neural network for detecting anomalies in discrete event sequences.
Specifically, OC4Seq integrates the anomaly detection objective with recurrent
neural networks (RNNs) to embed the discrete event sequences into latent
spaces, where anomalies can be easily detected. In addition, given that an
anomalous sequence could be caused by either individual events, subsequences of
events, or the whole sequence, we design a multi-scale RNN framework to capture
different levels of sequential patterns simultaneously. Experimental results on
three benchmark datasets show that OC4Seq consistently outperforms various
representative baselines by a large margin. Moreover, through both quantitative
and qualitative analysis, the importance of capturing multi-scale sequential
patterns for event anomaly detection is verified.
