Abstract
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.
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