An Adaptive Network Data Collection System in SDN

Donghao Zhou, Zheng Yan, Gao Liu, Mohammed Atiquzzaman

    Research output: Contribution to journalArticleScientificpeer-review

    22 Citations (Scopus)
    341 Downloads (Pure)

    Abstract

    Network data collection is a vital part in the process of network monitoring, traffic billing, network management and intrusion detection. As a new kind of network architecture, Software Defined Network (SDN) provides a possibility of intelligent and adaptive network data collection with centralized control and programming. However, existing literatures lack a concrete solution to economically collect network data, while satisfying the quality of data processing and analytics. Current data collection methods are not sufficiently adaptive and intelligent in terms of network context awareness. In this paper, we propose an adaptive network data collection system in SDN by automatically selecting proper data collection nodes based on network status in a dynamic way. During data collection, network traffic is sampled by considering flow characteristics in order to effectively reduce the amount of collected data while ensuring the accuracy of later data analysis, e.g., malicious traffic detection. A series of experiments are conducted to test and verify the data collection system and show its advantages through comparison with existing works in terms of CPU/memory consumption, storage usage, flow size recovery, and threat perception.
    Original languageEnglish
    Article number8915764
    Pages (from-to)562-574
    Number of pages13
    JournalIEEE Transactions on Cognitive Communications and Networking
    Volume6
    Issue number2
    Early online date2019
    DOIs
    Publication statusPublished - Jun 2020
    MoE publication typeA1 Journal article-refereed

    Keywords

    • SDN
    • network data collection
    • traffic characteristics.

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