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 over network performance data, gathered by perfSONAR, using two machine learning algorithms: Boosted Decision Trees (BDT) and Simple Feedforward Neural Network. The effectiveness of each algorithm was evaluated and compared. Both have shown sufficient performance and sensitivity.
@article{arxiv.1801.10094,
title = {Anomaly detection in wide area network mesh using two machine learning anomaly detection algorithms},
author = {James Zhang and Ilija Vukotic and Robert Gardner},
journal= {arXiv preprint arXiv:1801.10094},
year = {2018}
}