A study on genetic-fuzzy based automatic intrusion detection on network datasets

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

@InProceedings{Jabez:2012:ICSEMA,
  author =       "J. Jabez and G. S. A. Mala",
  booktitle =    "International Conference on Software Engineering and
                 Mobile Application Modelling and Development (ICSEMA
                 2012)",
  title =        "A study on genetic-fuzzy based automatic intrusion
                 detection on network datasets",
  year =         "2012",
  month =        dec,
  abstract =     "The intrusion detection aims at distinguishing the
                 attack data and the normal data from the network
                 pattern database. It is an indispensable part of the
                 information security system. Due to the variety of
                 network data behaviours and the rapid development of
                 attack fashions, it is necessary to develop a fast
                 machine-learning-based intrusion detection algorithm
                 with high detection rates and low false-alarm rates. In
                 this correspondence, we propose a novel fuzzy method
                 with genetic for detecting intrusion data from the
                 network database. Genetic algorithm is an evolutionary
                 optimisation technique, which uses Directed graph
                 structures instead of strings in genetic algorithm or
                 trees in genetic programming, which leads to enhancing
                 the representation ability with a compact programs
                 derived from the reusability of nodes in a graph
                 structure. By combining fuzzy set theory with Genetic
                 proposes a new method that can deal with a mixed of
                 database that contains both discrete and continuous
                 attributes and also extract many important association
                 rules to contribute and to enhance the Intrusion data
                 detections ability. Therefore, the proposed method is
                 flexible and can be applied for both misuse and anomaly
                 detection in data-intrusion-detection problems. Also
                 the incomplete database will include some of the
                 missing data in some tuples and however, the proposed
                 methods by applying some rules to extract these tuples.
                 The Genetic-Fuzzy presents a data Intrusion Detection
                 Systems for recovering data. It also include following
                 steps in Genetic-Fuzzy rules: Process data model as a
                 mathematical representation for Normal data.; Improving
                 the process data model which improves the Model of
                 normal data and it should represent the underlying
                 truth of normal Data.; Uses cluster centres or
                 centroids and use distances away from the centroids and
                 co",
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1049/ic.2012.0135",
  notes =        "Also known as \cite{6549299}",
}

Genetic Programming entries for J Jabez G S A Mala

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