  The increasing popularity of server usage has brought a plenty of anomaly log
events, which have threatened a vast collection of machines. Recognizing and
categorizing the anomalous events thereby is a much salient work for our
systems, especially the ones generate the massive amount of data and harness it
for technology value creation and business development. To assist in focusing
on the classification and the prediction of anomaly events, and gaining
critical insights from system event records, we propose a novel log
preprocessing method which is very effective to filter abundant information and
retain critical characteristics. Additionally, a competitive approach for
automated classification of anomalous events detected from the distributed
system logs with the state-of-the-art deep (Convolutional Neural Network)
architectures is proposed in this paper. We measure a series of deep CNN
algorithms with varied hyper-parameter combinations by using standard
evaluation metrics, the results of our study reveals the advantages and
potential capabilities of the proposed deep CNN models for anomaly event
classification tasks on real-world systems. The optimal classification
precision of our approach is 98.14%, which surpasses the popular traditional
machine learning methods.
