Anomaly detection in wide area network meshes using two machine learning algorithms

摘要

Anomaly detection is the practice of identifying items or events that do not conform to an expected behavior or do not correlate with other items in a dataset. It has previously been applied to areas such as intrusion detection, system health monitoring, and fraud detection in credit card transactions. In this paper, we describe a new method for detecting anomalous behavior in network performance data, gathered by the Open Science Grid using perfSONAR servers. Two machine learning algorithms were studied: a Boosted Decision Tree (BDT) and a simple feedforward neural network. The effectiveness of each algorithm was evaluated and compared. Both have shown sufficient performance and sensitivity. (C) 2018 Elsevier B.V. All rights reserved.