TeleDAL: a regression-based template-less unsupervised method for finding anomalies in log sequences

Several machine learning-based methods are available in the literature to find anomalies in large log sequences. Recently, deep learning based solutions demonstrated promising performance in this domain. The majority of these methods formulate the problem as a classification task, where the final layer of the neural network model is a softmax layer. This paper presents a different, regression-based approach. The input of the model is a sequence of numeric vectors representing the semantic information of the log lines, and the output is also a numeric vector corresponding to the expected log line. To cope with the inherent uncertainty of log sequences, we introduce “Top-K” layers, allowing the model to emit multiple predictions, from which the best one is chosen. This feature, together with the “Top-K” loss function, makes it possible to develop an unsupervised, fully template-less model that can also handle log lines it has never seen during the training phase, which is essential for online applications. On benchmark data sets we demonstrate that the model achieves competitive performance.