• DocumentCode
    1814522
  • Title

    Anomaly Traffic Detection Model Based on Dynamic Aggregation

  • Author

    Sun, Zhixin ; Gong, Jin

  • Author_Institution
    Coll. of Comput., Nanjing Univ. of Posts & Telecommun., Nanjing, China
  • fYear
    2010
  • fDate
    29-31 July 2010
  • Firstpage
    46
  • Lastpage
    50
  • Abstract
    Integrated with the ideas of aggregation and network model, this paper presented an anomaly detection model based on DAATDM, i.e. the dynamic and aggregate anomaly detection model. Besides, it established an anomaly traffic detection system based on DAATDM. DAATDM not only analyzed the aggregation of network parameters but also built a weighted statistical model for aggregate parameters which can be set dynamically. DAATDM can adjust its dependency on network parameters so as to enhance the flexibility of anomaly detection and identify attack features.
  • Keywords
    object detection; parameter estimation; statistical analysis; traffic engineering computing; DAATDM; anomaly traffic detection model; dynamic aggregation; network parameters; weighted statistical model; Aggregates; Computational modeling; Computer crime; Computers; Feature extraction; Floods; IP networks; Aggregation; Anomaly traffic; Dynamic detection;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Electronic Commerce and Security (ISECS), 2010 Third International Symposium on
  • Conference_Location
    Guangzhou
  • Print_ISBN
    978-1-4244-8231-3
  • Electronic_ISBN
    978-1-4244-8231-3
  • Type

    conf

  • DOI
    10.1109/ISECS.2010.19
  • Filename
    5557438