• DocumentCode
    2689165
  • Title

    A genetic algorithm for solving the binning problem in networked applications detection

  • Author

    Shevertalov, Maxim ; Stehle, Edward ; Mancoridis, Spiros

  • Author_Institution
    Drexel Univ., Philadelphia
  • fYear
    2007
  • fDate
    25-28 Sept. 2007
  • Firstpage
    713
  • Lastpage
    720
  • Abstract
    Network administrators need a tool that detects the kind of applications running on their networks, in order to allocate resources and enforce security policies. Previous work shows that applications can be detected by analyzing packet size distributions. Detection by packet size distribution is more efficient and accurate if the distribution is binned. An unbinned packet size distribution considers the occurrences of each packet size individually. In contrast, a binned packet size distribution considers the occurrences of packets within packet size ranges. This paper reviews some of the common methods for binning distributions and presents an improved approach to binning using a genetic algorithms to assist the detection of network applications.
  • Keywords
    genetic algorithms; resource allocation; security of data; binned packet size distribution; binning problem; genetic algorithm; network administrators; networked applications detection; resource allocation; security policies; Application software; Classification algorithms; Computer science; Educational institutions; Frequency; Genetic algorithms; Genetic engineering; Histograms; Resource management; Telecommunication traffic;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Evolutionary Computation, 2007. CEC 2007. IEEE Congress on
  • Conference_Location
    Singapore
  • Print_ISBN
    978-1-4244-1339-3
  • Electronic_ISBN
    978-1-4244-1340-9
  • Type

    conf

  • DOI
    10.1109/CEC.2007.4424541
  • Filename
    4424541