Training Classifiers to Identify TCP Signatures in Scientific Workflows Identifying network anomalies is an important mea sure to ensure reliability and quality of data transfers among facilities. Scientific workflows in particular heavily rely on good network performance to ensure their smooth executions. In this paper, we present a lightweight classifier system that is able to recognize anomalous TCP transfers. Using random forest trees and labeled data sets, we evaluate the classifier with real workflow transfers for ground truth data. Our studies reveal that various TCP congestion algorithms behave differently in anomalous conditions. We show that training classifiers on these separately can aid detection in network performance deterioration. Results reveal that our classifiers are able to better predict anomalous flows for TCP Reno and Hamilton compared to Cubic and BBR, due to the manner how their congestion control algorithms handle the anomalies.

Integrating Machine Learning with OfftheShelf Traffic Flow Features for HTTPHTTPS Traffic Classification Accurate traffic classification is a key requirement for different network and security monitoringplanning tools. The evolution of Internet protocols and applications has caused traditional traffic classification approaches to be ineffective in certain cases. Key causes of the inaccuracy include: (i) the increase in the encrypted traffic; (ii) the rise in the usage of dynamic port numbers for different applications; and (iii) multiple applications running over HTTPHTTPS protocols. Traditional solutions for traffic analysis, classification, and measurement fall short in providing visibility in usersxe2x80x99 activities  a key requirement for network and security monitoring tools. In this paper, we evaluate an automatic classifier for encrypted Social media, Video and Audio traffic without relying on particular application layer header fields that can be easily modified. We leverage machine learning algorithms together with the features provided by the wellknown offtheshelf traffic flow exporters. We evaluate the performance of such a system also for generalization (robustness) purposes on different networks. Experimental results show promising performances in terms of generating robust traffic classification on large traffic data when the trained model is moved to different networks.