  Logs are semi-structured text files that represent software's execution paths
and states during its run-time. Therefore, detecting anomalies in software logs
reflect anomalies in the software's execution path or state. So, it has become
a notable concern in software engineering. We use LSTM like many prior works,
and on top of LSTM, we propose a novel anomaly detection approach based on the
Siamese network. This paper also provides an authentic validation of the
approach on the Hadoop Distributed File System (HDFS) log dataset. To the best
of our knowledge, the proposed approach outperforms other methods on the same
dataset at the F1 score of 0.996, resulting in a new state-of-the-art
performance on the dataset. Along with the primary method, we introduce a novel
training pair generation algorithm that reduces generated training pairs by the
factor of 3000 while maintaining the F1 score, merely a modest decay from 0.996
to 0.995. Additionally, we propose a hybrid model by combining the Siamese
network with a traditional feedforward neural network to make end-to-end
training possible, reducing engineering effort in setting up a
deep-learning-based log anomaly detector. Furthermore, we examine our method's
robustness to log evolutions by evaluating the model on synthetically evolved
log sequences; we got the F1 score of 0.95 at the noise ratio of 20%. Finally,
we dive deep into some of the side benefits of the Siamese network.
Accordingly, we introduce a method of monitoring the evolutions of logs without
label requirements at run-time. Additionally, we present a visualization
technique that facilitates human administrations of log anomaly detection.
