Anomaly detection in wide area network mesh using two machine learning anomaly detection algorithms
This addresses network monitoring for IT administrators, but it is incremental as it applies existing methods to new data.
The paper tackled anomaly detection in wide area network mesh using perfSONAR data, comparing Boosted Decision Trees and Simple Feedforward Neural Network, with both algorithms showing sufficient performance and sensitivity.
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.