HB2DS: A behavior-driven high-bandwidth network mining system
This paper proposes a behavior detection system, HB2DS, to address the behavior-detection challenges in high-bandwidth networks. In HB2DS, a summarization of network traffic is represented through some meta-events. The relationships amongst meta-events are used to mine end-user behaviors. HB2DS satisfies the main constraints exist in analyzing of high-bandwidth networks, namely online learning and outlier handling, as well as one-pass processing, delay, and memory limitations. Our evaluation indicates significant improvement in big data stream analyzing in terms of accuracy and efficiency.