• DocumentCode
    1675433
  • Title

    Fuzzy frequent episodes for real-time intrusion detection

  • Author

    Luo, Jianxiong ; Bridges, Susan M. ; Vaughn, Rayford B., Jr.

  • Author_Institution
    Dept. of Comput. Sci., Mississippi State Univ., MS, USA
  • Volume
    1
  • fYear
    2001
  • fDate
    6/23/1905 12:00:00 AM
  • Firstpage
    368
  • Lastpage
    371
  • Abstract
    Data mining methods including association rule mining and frequent episode mining have been applied to the intrusion detection problem. We describe an extension that uses fuzzy frequent episodes for near real-time intrusion detection. We first define fuzzy frequent episodes and then describe experiments that explore their applicability for real-time intrusion detection. Experimental results indicate that fuzzy frequent episodes can provide effective approximate anomaly detection
  • Keywords
    data mining; fuzzy set theory; security of data; approximate anomaly detection; association rule mining; data mining methods; frequent episode mining; fuzzy frequent episodes; real-time intrusion detection; Association rules; Bridges; Computer networks; Computer science; Data mining; Frequency; IP networks; Intrusion detection; Modems; Quantization;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Fuzzy Systems, 2001. The 10th IEEE International Conference on
  • Conference_Location
    Melbourne, Vic.
  • Print_ISBN
    0-7803-7293-X
  • Type

    conf

  • DOI
    10.1109/FUZZ.2001.1007325
  • Filename
    1007325
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1675433