Open AccessInline high‐bandwidth network analysis using a robust stream clustering algorithm
Author(s) -
Noferesti Morteza,
Jalili Rasool
Publication year - 2019
Publication title -
iet information security
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.308
H-Index - 34
eISSN - 1751-8717
pISSN - 1751-8709
DOI - 10.1049/iet-ifs.2018.5287
Subject(s) - computer science , network traffic control , cluster analysis , bandwidth (computing) , dynamic bandwidth allocation , data mining , distributed computing , sliding window protocol , robustness (evolution) , outlier , bandwidth management , real time computing , computer network , algorithm , artificial intelligence , network packet , window (computing) , biochemistry , chemistry , gene , operating system
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.