Network intrusion detection using fuzzy class association rule mining based on genetic network programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4221

  author =       "Ci Chen and Shingo Mabu and Chuan Yue and 
                 Kaoru Shimada and Kotaro Hirasawa",
  title =        "Network intrusion detection using fuzzy class
                 association rule mining based on genetic network
  booktitle =    "IEEE International Conference on Systems, Man and
                 Cybernetics, SMC 2009",
  year =         "2009",
  pages =        "60--67",
  address =      "San Antonio, Texas, USA",
  month =        oct,
  keywords =     "genetic algorithms, genetic programming, Internet,
                 anomaly detection, computer systems, directed graph
                 structure, evolutionary optimization, fuzzy class
                 association rule mining, fuzzy set theory, genetic
                 network programming, machine learning, network
                 intrusion detection, Internet, data mining, security of
  DOI =          "doi:10.1109/ICSMC.2009.5346328",
  ISSN =         "1062-922X",
  abstract =     "Computer systems are exposed to an increasing number
                 and type of security threats due to the expanding of
                 Internet in recent years. How to detect network
                 intrusions effectively becomes an important techniques.
                 This paper presents a novel fuzzy class association
                 rule mining method based on Genetic Network Programming
                 (GNP) for detecting network intrusions. GNP is an
                 evolutionary optimization techniques, which uses
                 directed graph structures as genes instead of strings
                 (Genetic Algorithm) or trees (Genetic Programming),
                 leading to creating compact programs and implicitly
                 memorizing past action sequences. By combining fuzzy
                 set theory with GNP, the proposed method can deal with
                 the mixed database which contains both discrete and
                 continuous attributes. And it can be flexibly applied
                 to both misuse and anomaly detection in Network
                 Intrusion Detection Problem. Experimental results with
                 KDD99Cup and DAPRA98 databases from MIT Lincoln
                 Laboratory show that the proposed method provides a
                 competitively high detection rate compared with other
                 machine learning techniques.",
  notes =        "Also known as \cite{5346328}",

Genetic Programming entries for Ci Chen Shingo Mabu Chuan Yue Kaoru Shimada Kotaro Hirasawa