Hybridization of Mean Shift Clustering and Deep Packet Inspected Classification for Network Traffic Analysis

Network traffic processing is an automated method for arranging and optimizing network traffic, based on the parameters. The traffic data is gathered to begin the study of the component of network traffic. Subsequently, the clustering and grouping process is carried out to evaluate network traffic....

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Bibliographic Details
Published in:Wireless personal communications 2022-11, Vol.127 (1), p.217-233
Main Authors: Kumar, Sathish A. P., Suresh, A., Anand, S. Raj, Chokkanathan, K., Vijayasarathy, M.
Format: Article
Language:English
Subjects:
Classification
Clustering
Communications Engineering
Communications traffic
Computer Communication Networks
Data points
Engineering
Evaluation
Inspection
Networks
Signal,Image and Speech Processing
Traffic analysis
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Summary:Network traffic processing is an automated method for arranging and optimizing network traffic, based on the parameters. The traffic data is gathered to begin the study of the component of network traffic. Subsequently, the clustering and grouping process is carried out to evaluate network traffic. Continuous evaluation of the patterns of network traffic remained a daunting challenge during traffic classification. However, existing approaches have not been able to reduce time consumption and improve clustering accuracy for network traffic analysis. In order to resolve these problems, a Density-based Mean Shift Clustering and Deep Packet Inspection Classification (DMSC-DPIC) methodology is implemented to perform an efficient network traffic analysis. In addition, the classification model DPI has been developed to identify network Traffic by payloading data points with minimum time as real as well as non-real-time traffic. In the DPI classification model, data points are grouped into various groups by analyzing associated points throughout the session. The experimental assessment of the proposed methodology DMSC-DPIC is carried out with the CAIDA anonymized Internet Traces Dataset and achieves improved efficiency compared with state-of-the-art work in terms of clustering precision, classification time and communications overhead.
ISSN:0929-6212
1572-834X
DOI:10.1007/s11277-021-08208-6