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HB2DS: a behavior-driven high-bandwidth network mining system

Noferesti, M ; Sharif University of Technology | 2017

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  1. Type of Document: Article
  2. DOI: 10.1016/j.jss.2016.07.004
  3. Publisher: Elsevier Inc , 2017
  4. Abstract:
  5. 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. © 2016 Elsevier Inc
  6. Keywords:
  7. Big data stream ; Network analysis ; Bandwidth ; Behavioral research ; Data communication systems ; Electric network analysis ; Behavior detection ; Data stream ; Data stream clustering ; End users ; High-bandwidth networks ; Mining systems ; Network traffic ; Online learning ; Big data
  8. Source: Journal of Systems and Software ; Volume 127 , 2017 , Pages 266-277 ; 01641212 (ISSN)
  9. URL: https://www.sciencedirect.com/science/article/pii/S0164121216301042