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

Abstract

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.