Applying Genetic Programming to Evolve Learned Rules for Network Anomaly Detection

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{conf/icnc/YinTHH05,
  title =        "Applying Genetic Programming to Evolve Learned Rules
                 for Network Anomaly Detection",
  author =       "Chuanhuan Yin and Shengfeng Tian and Houkuan Huang and 
                 Jun He",
  year =         "2005",
  pages =        "323--331",
  editor =       "Lipo Wang and Ke Chen and Yew-Soon Ong",
  publisher =    "Springer",
  series =       "Lecture Notes in Computer Science",
  volume =       "3612",
  booktitle =    "Advances in Natural Computation, First International
                 Conference, ICNC 2005, Proceedings, Part III",
  address =      "Changsha, China",
  month =        aug # " 27-29",
  bibdate =      "2005-08-01",
  bibsource =    "DBLP,
                 http://dblp.uni-trier.de/db/conf/icnc/icnc2005-3.html#YinTHH05",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-28320-X",
  DOI =          "doi:10.1007/11539902_38",
  size =         "9 pages",
  abstract =     "The DARPA/MIT Lincoln Laboratory off-line intrusion
                 detection evaluation data set is the most widely used
                 public benchmark for testing intrusion detection
                 systems. But the presence of simulation artifacts
                 attributes would cause many attacks in this dataset to
                 be easily detected. In order to eliminate their
                 influence on intrusion detection, we simply omit these
                 attributes in the processes of both training and
                 testing. We also present a GP-based rule learning
                 approach for detecting attacks on network. GP is used
                 to evolve new rules from the initial learned rules
                 through genetic operations. Our results show that
                 GP-based rule learning approach outperforms the
                 original rule learning algorithm, detecting 84 of 148
                 attacks at 100 false alarms despite the absence of
                 several simulation artifacts attributes.",
}

Genetic Programming entries for Chuanyuan Yin Shengfeng Tian Houkuan Huang Jun He

Citations