hybrid feature selection algorithm for intrusion detection system

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

  author =       "Seyed Reza Hasani and Zulaiha Ali Othman and 
                 Seyed Mostafa Mousavi Kahaki",
  title =        "hybrid feature selection algorithm for intrusion
                 detection system",
  journal =      "Journal of Computer Science",
  publisher =    "Science Publications",
  year =         "2014",
  volume =       "10",
  number =       "6",
  pages =        "1015--1025",
  keywords =     "genetic algorithms, genetic programming",
  ISSN =         "1549-3636",
  bibsource =    "OAI-PMH server at doaj.org",
  language =     "English",
  oai =          "oai:doaj.org/article:2b97571506b643c1881e8a9bdb4636a6",
  URL =          "http://www.thescipub.com/pdf/10.3844/jcssp.2014.1015.1025",
  DOI =          "DOI:10.3844/jcssp.2014.1015.1025",
  size =         "11 pages",
  abstract =     "Network security is a serious global concern.
                 Usefulness Intrusion Detection Systems (IDS) are
                 increasing incredibly in Information Security research
                 using Soft computing techniques. In the previous
                 researches having irrelevant and redundant features are
                 recognised causes of increasing the processing speed of
                 evaluating the known intrusive patterns. In addition,
                 an efficient feature selection method eliminates
                 dimension of data and reduce redundancy and ambiguity
                 caused by none important attributes. Therefore, feature
                 selection methods are well-known methods to overcome
                 this problem. There are various approaches being used
                 in intrusion detections, they are able to perform their
                 method and relatively they are achieved with some
                 improvements. This work is based on the enhancement of
                 the highest Detection Rate (DR) algorithm which is
                 Linear Genetic Programming (LGP) reducing the False
                 Alarm Rate (FAR) incorporates with Bees Algorithm.
                 Finally, Support Vector Machine (SVM) is one of the
                 best candidate solutions to settle IDSs problems. In
                 this study four sample dataset containing 4000 random
                 records are excluded randomly from this dataset for
                 training and testing purposes. Experimental results
                 show that the LGP_BA method improves the accuracy and
                 efficiency compared with the previous related research
                 and the feature subcategory offered by LGP_BA gives a
                 superior representation of data.",

Genetic Programming entries for Seyed Reza Hasani Zulaiha Ali Othman Seyed Mostafa Mousavi Kahaki