Please use this identifier to cite or link to this item: https://dspace.iiti.ac.in/handle/123456789/4585
Title: Efficient Keyword Matching for Deep Packet Inspection based Network Traffic Classification
Authors: Khandait, Pratibha
Hubballi, Neminath
Mazumdar, Bodhisatwa
Keywords: Heuristic methods;Inspection;Large dataset;Network security;Packet networks;Application signatures;Classification accuracy;Deep packet inspection;Deep packet inspection (DPI);Network traffic classification;State transitions;String matching;Traffic classification;Classification (of information)
Issue Date: 2020
Publisher: Institute of Electrical and Electronics Engineers Inc.
Citation: Khandait, P., Hubballi, N., & Mazumdar, B. (2020). Efficient keyword matching for deep packet inspection based network traffic classification. Paper presented at the 2020 International Conference on COMmunication Systems and NETworkS, COMSNETS 2020, 567-570. doi:10.1109/COMSNETS48256.2020.9027353
Abstract: Network traffic classification has a range of applications in network management including QoS and security monitoring. Deep Packet Inspection (DPI) is one of the effective method used for traffic classification. DPI is computationally expensive operation involving string matching between payload and application signatures. Existing traffic classification techniques perform multiple scans of payload to classify the application flows - first scan to extract the words and the second scan to match the words with application signatures. In this paper we propose an approach which can classify network flows with single scan of flow payloads using a heuristic method to achieve a sub-linear search complexity. The idea is to scan few initial bytes of payload and determine potential application signature(s) for subsequent signature matching. We perform experiments with a large dataset containing 171873 network flows and show that it has a good classification accuracy of 98%. © 2020 IEEE.
URI: https://doi.org/10.1109/COMSNETS48256.2020.9027353
https://dspace.iiti.ac.in/handle/123456789/4585
ISBN: 9781728131870
Type of Material: Conference Paper
Appears in Collections:Department of Computer Science and Engineering

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