Deep Convolutional Neural Networks for Anomaly Event Classification on Distributed Systems
This work addresses the challenge of managing and categorizing anomaly events in distributed systems, which is critical for technology value creation and business development, but it appears incremental as it builds on existing deep CNN architectures.
The paper tackles the problem of classifying anomaly events in distributed system logs by proposing a novel log preprocessing method and using deep convolutional neural networks, achieving an optimal classification precision of 98.14% that surpasses traditional machine learning methods.
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.