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MPTCP-meLearning: A Multi-Expert Learning-Based MPTCP Extension to Enhance Multipathing Robustness against Network Attacks

Yuanlong CAO, Ruiwen JI, Lejun JI, Xun SHAO, Gang LEI, Hao WANG

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Summary :

With multiple network interfaces are being widely equipped in modern mobile devices, the Multipath TCP (MPTCP) is increasingly becoming the preferred transport technique since it can uses multiple network interfaces simultaneously to spread the data across multiple network paths for throughput improvement. However, the MPTCP performance can be seriously affected by the use of a poor-performing path in multipath transmission, especially in the presence of network attacks, in which an MPTCP path would abrupt and frequent become underperforming caused by attacks. In this paper, we propose a multi-expert Learning-based MPTCP variant, called MPTCP-meLearning, to enhance MPTCP performance robustness against network attacks. MPTCP-meLearning introduces a new kind of predictor to possibly achieve better quality prediction accuracy for each of multiple paths, by leveraging a group of representative formula-based predictors. MPTCP-meLearning includes a novel mechanism to intelligently manage multiple paths in order to possibly mitigate the out-of-order reception and receive buffer blocking problems. Experimental results demonstrate that MPTCP-meLearning can achieve better transmission performance and quality of service than the baseline MPTCP scheme.

Publication
IEICE TRANSACTIONS on Information Vol.E104-D No.11 pp.1795-1804
Publication Date
2021/11/01
Publicized
2021/07/08
Online ISSN
1745-1361
DOI
10.1587/transinf.2021NGP0009
Type of Manuscript
Special Section PAPER (Special Section on Next-generation Security Applications and Practice)
Category

Authors

Yuanlong CAO
  Jiangxi Normal University
Ruiwen JI
  Jiangxi Normal University
Lejun JI
  Jiangxi Normal University
Xun SHAO
  Kitami Institute of Technology
Gang LEI
  Jiangxi Normal University
Hao WANG
  Jiangxi Normal University

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