Continuous, dynamic and short-term learning is an effective learning strategy when operating in very fast and dynamic environments, where concept drift constantly occurs. We focus on a particularly challenging problem, that of continually learning detection models capable to recognize network attacks and system intrusions in highly dynamic environments such as communication networks. We consider adaptive learning algorithms for the analysis of continuously evolving network data streams, using a dynamic, variable length system memory which automatically adapts to concept drifts in the underlying data. By continuously learning and detecting concept drifts to adapt memory length, we show that adaptive learning algorithms can continuously realize high detection accuracy over dynamic network data streams. To deal with big network traffic streams, we deploy the proposed models into a big data analytics platform for network traffic monitoring and analysis tasks, and show that high speed up computations (as high as × 5) can be achieved by parallelizing off-the-shelf stream learning approaches.
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- NetSEC at High-Speed: Distributed Stream Learning for Security in Big Networking Data
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