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Title: An approach to online network monitoring using clustered patterns

Network traffic monitoring is a core element in network operations and management for various purposes such as anomaly detection, change detection, and fault/failure detection. In this study, we introduce a new approach to online monitoring using a pattern-based representation of the network traffic. Unlike the past online techniques limited to a single variable to summarize (e.g., sketch), the focus of this study is on capturing the network state from the multivariate attributes under consideration. To this end, we employ clustering with its benefit of the aggregation of multidimensional variables. The clustered result represents the state of the network with regard to the monitored variables, which can also be compared with the previously observed patterns visually and quantitatively. Finally, we demonstrate the proposed method with two popular use cases, one for estimating state changes and the other for identifying anomalous states, to confirm its feasibility.
Authors:
 [1] ;  [2] ;  [1] ;  [3]
  1. Texas A & M Univ., Commerce, TX (United States)
  2. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
  3. Electronics and Telecommunications Research Inst. (ETRI), Daejeon (Korea, Republic of)
Publication Date:
Grant/Contract Number:
AC02-05CH11231; B0101-15-1293
Type:
Accepted Manuscript
Journal Name:
2017 International Conference on Computing, Networking and Communications, ICNC 2017
Additional Journal Information:
Journal Name: 2017 International Conference on Computing, Networking and Communications, ICNC 2017
Research Org:
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Electronics and Telecommunications Research Inst. (ETRI), Daejeon (Korea, Republic of)
Sponsoring Org:
USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR) (SC-21); Ministry of Science, ICT and Future Planning (MSIP) of Korea
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; monitoring; quality of service; systems modeling; electric breakdown; computer crime; timing; system monitoring; computer network management; pattern clustering
OSTI Identifier:
1379769

Kim, Jinoh, Sim, Alex, Suh, Sang C., and Kim, Ikkyun. An approach to online network monitoring using clustered patterns. United States: N. p., Web. doi:10.1109/ICCNC.2017.7876207.
Kim, Jinoh, Sim, Alex, Suh, Sang C., & Kim, Ikkyun. An approach to online network monitoring using clustered patterns. United States. doi:10.1109/ICCNC.2017.7876207.
Kim, Jinoh, Sim, Alex, Suh, Sang C., and Kim, Ikkyun. 2017. "An approach to online network monitoring using clustered patterns". United States. doi:10.1109/ICCNC.2017.7876207. https://www.osti.gov/servlets/purl/1379769.
@article{osti_1379769,
title = {An approach to online network monitoring using clustered patterns},
author = {Kim, Jinoh and Sim, Alex and Suh, Sang C. and Kim, Ikkyun},
abstractNote = {Network traffic monitoring is a core element in network operations and management for various purposes such as anomaly detection, change detection, and fault/failure detection. In this study, we introduce a new approach to online monitoring using a pattern-based representation of the network traffic. Unlike the past online techniques limited to a single variable to summarize (e.g., sketch), the focus of this study is on capturing the network state from the multivariate attributes under consideration. To this end, we employ clustering with its benefit of the aggregation of multidimensional variables. The clustered result represents the state of the network with regard to the monitored variables, which can also be compared with the previously observed patterns visually and quantitatively. Finally, we demonstrate the proposed method with two popular use cases, one for estimating state changes and the other for identifying anomalous states, to confirm its feasibility.},
doi = {10.1109/ICCNC.2017.7876207},
journal = {2017 International Conference on Computing, Networking and Communications, ICNC 2017},
number = ,
volume = ,
place = {United States},
year = {2017},
month = {3}
}