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One of the most challenging problems in the field of intrusion detection isanomaly detection for discrete event logs. While most earlier work focused onapplying unsupervised learning upon engineered features, most recent work hasstarted to resolve this challenge by applying deep learning methodology toabstraction of discrete event entries. Inspired by natural language processing,LSTM-based anomaly detection models were proposed. They try to predict upcomingevents, and raise an anomaly alert when a prediction fails to meet a certaincriterion. However, such a predict-next-event methodology has a fundamentallimitation: event predictions may not be able to fully exploit the distinctivecharacteristics of sequences. This limitation leads to high false positives(FPs) and high false negatives (FNs). It is also critical to examine thestructure of sequences and the bi-directional causality among individualevents. To this end, we propose a new methodology: Recomposing event sequencesas anomaly detection. We propose DabLog, a Deep Autoencoder-Based anomalydetection method for discrete event Logs. The fundamental difference is that,rather than predicting upcoming events, our approach determines whether asequence is normal or abnormal by analyzing (encoding) and reconstructing(decoding) the given sequence. Our evaluation results show that our newmethodology can significantly reduce the numbers of FPs and FNs, henceachieving a higher $F_1$ score.

Recomposition vs. Prediction: A Novel Anomaly Detection for Discrete Events Based On Autoencoder