LAUSR

Network-monitoring data commonly arrives in the form of fast and changing data streams. Continuous and dynamic learning is an effective learning strategy when dealing with such data, where concept drifts constantly occur. We propose different stream-based, adaptive learning approaches to analyze network-traffic streams on the fly. We address two major challenges associated to stream-based machine learning and online network monitoring: (i) how to dynamically learn from and adapt to non-stationary data changing over time, and (ii) how to deal with the limited availability of labeled data to continuously tune a supervised-learning model. We introduce ADAM & RAL, two stream-based machine-learning techniques to tackle these challenges. ADAM relies on adaptive memory strategies to dynamically tune stream-based learning models to changes in the input data distribution. RAL combines reinforcement learning with stream-based active-learning to reduce the amount of labeled data needed for continual learning, dynamically deciding on the most informative samples to learn from. We apply ADAM & RAL to the real-time detection of network attacks in Internet network traffic, and show that it is possible to continuously achieve high detection accuracy even under the occurrence of concept drifts, limiting the amount of labeled data needed for learning.