Evaluation of Multilayer Perceptron algorithms for an analysis of network flow data

In this study we evaluated a machine learning approach for detection of anomalies in IP network data represented as NetFlow records. We considered Multilayer Perceptron (MLP) as the classifier and we used two types of learning algorithms – Backpropagation (BP) and Particle Swarm Optimization (PSO). This study includes a comprehensive survey on determining the most optimal MLP learning algorithm for the classification problem in application to network flow data. The performance, training time and convergence of BP and PSO methods were compared. The results show that PSO algorithm implemented by the authors outperformed other solutions if accuracy of classifications is considered. The major disadvantage of PSO is training time, which could be not acceptable for larger data sets or in real network applications. At the end we compared some key findings with the results from the other papers to show that in all cases results from this study outperformed them.

Autors: Jędrzej Bieniasz, Mariusz Rawski, Krzysztof Skowron, Mateusz Trzepiński