A study on genetic-fuzzy based automatic intrusion detection on network datasets
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- @InProceedings{Jabez:2012:ICSEMA,
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author = "J. Jabez and G. S. A. Mala",
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booktitle = "International Conference on Software Engineering and
Mobile Application Modelling and Development (ICSEMA
2012)",
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title = "A study on genetic-fuzzy based automatic intrusion
detection on network datasets",
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year = "2012",
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month = dec,
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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",
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keywords = "genetic algorithms, genetic programming",
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DOI = "doi:10.1049/ic.2012.0135",
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notes = "Also known as \cite{6549299}",
- }
Genetic Programming entries for
J Jabez
G S A Mala
Citations