Anomaly identification based on flow analysis

The Quyent Le, Marat Zhanikeev, Yoshiaki Tanaka

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    5 Citations (Scopus)

    Abstract

    Recently, the concern about network management and network security has inspired many research topics, among which, research on detecting and identifying anomalies has attracted a lot of interest. In this paper, we propose an anomaly identification method based on traffic flow analysis. The result of the research proves that by assembling some features of traffic flows and calculating some specific extended metrics, we can successfully detect and identify network anomalies with high accuracy. Besides, the method also proves its efficiency with the ability to quantify network anomalies and locate relevant network nodes. The research validity has been verified by both simulation data and real network data.

    Original languageEnglish
    Title of host publicationIEEE Region 10 Annual International Conference, Proceedings/TENCON
    DOIs
    Publication statusPublished - 2007
    Event2006 IEEE Region 10 Conference, TENCON 2006 - Hong Kong
    Duration: 2006 Nov 142006 Nov 17

    Other

    Other2006 IEEE Region 10 Conference, TENCON 2006
    CityHong Kong
    Period06/11/1406/11/17

    Fingerprint

    Network security
    Network management

    ASJC Scopus subject areas

    • Engineering(all)

    Cite this

    Le, T. Q., Zhanikeev, M., & Tanaka, Y. (2007). Anomaly identification based on flow analysis. In IEEE Region 10 Annual International Conference, Proceedings/TENCON [4142352] https://doi.org/10.1109/TENCON.2006.344124

    Anomaly identification based on flow analysis. / Le, The Quyent; Zhanikeev, Marat; Tanaka, Yoshiaki.

    IEEE Region 10 Annual International Conference, Proceedings/TENCON. 2007. 4142352.

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Le, TQ, Zhanikeev, M & Tanaka, Y 2007, Anomaly identification based on flow analysis. in IEEE Region 10 Annual International Conference, Proceedings/TENCON., 4142352, 2006 IEEE Region 10 Conference, TENCON 2006, Hong Kong, 06/11/14. https://doi.org/10.1109/TENCON.2006.344124
    Le TQ, Zhanikeev M, Tanaka Y. Anomaly identification based on flow analysis. In IEEE Region 10 Annual International Conference, Proceedings/TENCON. 2007. 4142352 https://doi.org/10.1109/TENCON.2006.344124
    Le, The Quyent ; Zhanikeev, Marat ; Tanaka, Yoshiaki. / Anomaly identification based on flow analysis. IEEE Region 10 Annual International Conference, Proceedings/TENCON. 2007.
    @inproceedings{3470cd8d05f040f095109185c89de8a1,
    title = "Anomaly identification based on flow analysis",
    abstract = "Recently, the concern about network management and network security has inspired many research topics, among which, research on detecting and identifying anomalies has attracted a lot of interest. In this paper, we propose an anomaly identification method based on traffic flow analysis. The result of the research proves that by assembling some features of traffic flows and calculating some specific extended metrics, we can successfully detect and identify network anomalies with high accuracy. Besides, the method also proves its efficiency with the ability to quantify network anomalies and locate relevant network nodes. The research validity has been verified by both simulation data and real network data.",
    author = "Le, {The Quyent} and Marat Zhanikeev and Yoshiaki Tanaka",
    year = "2007",
    doi = "10.1109/TENCON.2006.344124",
    language = "English",
    isbn = "1424405491",
    booktitle = "IEEE Region 10 Annual International Conference, Proceedings/TENCON",

    }

    TY - GEN

    T1 - Anomaly identification based on flow analysis

    AU - Le, The Quyent

    AU - Zhanikeev, Marat

    AU - Tanaka, Yoshiaki

    PY - 2007

    Y1 - 2007

    N2 - Recently, the concern about network management and network security has inspired many research topics, among which, research on detecting and identifying anomalies has attracted a lot of interest. In this paper, we propose an anomaly identification method based on traffic flow analysis. The result of the research proves that by assembling some features of traffic flows and calculating some specific extended metrics, we can successfully detect and identify network anomalies with high accuracy. Besides, the method also proves its efficiency with the ability to quantify network anomalies and locate relevant network nodes. The research validity has been verified by both simulation data and real network data.

    AB - Recently, the concern about network management and network security has inspired many research topics, among which, research on detecting and identifying anomalies has attracted a lot of interest. In this paper, we propose an anomaly identification method based on traffic flow analysis. The result of the research proves that by assembling some features of traffic flows and calculating some specific extended metrics, we can successfully detect and identify network anomalies with high accuracy. Besides, the method also proves its efficiency with the ability to quantify network anomalies and locate relevant network nodes. The research validity has been verified by both simulation data and real network data.

    UR - http://www.scopus.com/inward/record.url?scp=34547559957&partnerID=8YFLogxK

    UR - http://www.scopus.com/inward/citedby.url?scp=34547559957&partnerID=8YFLogxK

    U2 - 10.1109/TENCON.2006.344124

    DO - 10.1109/TENCON.2006.344124

    M3 - Conference contribution

    SN - 1424405491

    SN - 9781424405497

    BT - IEEE Region 10 Annual International Conference, Proceedings/TENCON

    ER -