Improving Anomalous Rare Attack Detection Rate for Intrusion Detection System Using Support Vector Machine and Genetic Programming

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@Article{journals/npl/PoziSMP16,
  author =       "Muhammad Syafiq Mohd Pozi and Md Nasir Sulaiman and 
                 Norwati Mustapha and Thinagaran Perumal",
  title =        "Improving Anomalous Rare Attack Detection Rate for
                 Intrusion Detection System Using Support Vector Machine
                 and Genetic Programming",
  journal =      "Neural Processing Letters",
  year =         "2016",
  number =       "2",
  volume =       "44",
  pages =        "279--290",
  month =        oct,
  keywords =     "genetic algorithms, genetic programming, IDS, NSL-KDD,
                 rare attacks, imbalanced class, SVM",
  ISSN =         "1370-4621",
  bibdate =      "2017-05-17",
  bibsource =    "DBLP,
                 http://dblp.uni-trier.de/https://doi.org/10.1007/s11063-015-9457-y;
                 DBLP,
                 http://dblp.uni-trier.de/db/journals/npl/npl44.html#PoziSMP16",
  DOI =          "doi:10.1007/s11063-015-9457-y",
  size =         "12 pages",
  abstract =     "Commonly addressed problem in intrusion detection
                 system (IDS) research works that employed NSL-KDD
                 dataset is to improve the rare attacks detection rate.
                 However, some of the rare attacks are hard to be
                 recognised by the IDS model due to their patterns are
                 totally missing from the training set, hence, reducing
                 the rare attacks detection rate. This problem of
                 missing rare attacks can be defined as anomalous rare
                 attacks and hardly been solved in IDS literature.
                 Hence, in this letter, we proposed a new classifier to
                 improve the anomalous attacks detection rate based on
                 support vector machine (SVM) and genetic programming
                 (GP). Based on the experimental results, our
                 classifier, GPSVM, managed to get higher detection rate
                 on the anomalous rare attacks, without significant
                 reduction on the overall accuracy. This is because,
                 GPSVM optimisation task is to ensure the accuracy is
                 balanced between classes without reducing the
                 generalisation property of SVM.",
}

Genetic Programming entries for Muhammad Syafiq Mohd Pozi Md Nasir Sulaiman Norwati Mustapha Thinagaran Perumal

Citations