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Inline high-bandwidth network analysis using a robust stream clustering algorithm

Noferesti, M ; Sharif University of Technology | 2019

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  1. Type of Document: Article
  2. DOI: 10.1049/iet-ifs.2018.5287
  3. Publisher: Institution of Engineering and Technology , 2019
  4. Abstract:
  5. High-bandwidth network analysis is challenging, resource consuming, and inaccurate due to the high volume, velocity, and variety characteristics of the network traffic. The infinite stream of incoming traffic forms a dynamic environment with unexpected changes, which requires analysing approaches to satisfy the high-bandwidth network processing challenges such as incremental learning, inline processing, and outlier handling. This study proposes an inline high-bandwidth network stream clustering algorithm designed to incrementally mine large amounts of continuously transmitting network traffic when some outliers can be dropped before determining the network traffic behaviour. Maintaining extended-meta-events as abstracting data structures over a sliding window, enriches the algorithm to address the high-bandwidth network processing challenges. Evaluating the algorithm indicates its robustness, efficiency, and accuracy in analysing high-bandwidth networks. © The Institution of Engineering and Technology 2019
  6. Keywords:
  7. Bandwidth ; Statistics ; Dynamic environments ; High-bandwidth networks ; Incoming traffic ; Incremental learning ; Inline processing ; Network traffic ; Robust stream ; Sliding Window ; Clustering algorithms
  8. Source: IET Information Security ; Volume 13, Issue 5 , 2019 , Pages 486-497 ; 17518709 (ISSN)
  9. URL: https://ieeexplore.ieee.org/document/8793284