Hybrid evolutionary algorithms for data classification in intrusion detection systems
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @InProceedings{Hedar:2015:ieee/acisSNPD,
-
author = "Abdel-Rahman Hedar and Mohamed A. Omer and
Ahmed F. Al-Sadek and Adel A. Sewisy",
-
booktitle = "16th IEEE/ACIS International Conference on Software
Engineering, Artificial Intelligence, Networking and
Parallel/Distributed Computing (SNPD)",
-
title = "Hybrid evolutionary algorithms for data classification
in intrusion detection systems",
-
year = "2015",
-
abstract = "Intrusion detection systems (IDS) are important to
protect our systems and networks from attacks and
malicious behaviours. In this paper, we propose a new
hybrid intrusion detection system by using accelerated
genetic algorithm and rough set theory (AGAAR) for data
feature reduction, and genetic programming with local
search (GPLS) for data classification. The AGAAR method
is used to select the most relevant attributes that can
represent an intrusion detection dataset. In order to
improve the performance of GPLS classifier, a new local
search strategy is used with genetic programming
operators. The main target of using local search
strategy is to discover the better solution from the
current. The results shown later indicate that
classification accuracy improved from 75.98percent to
81.44percent after using AGAAR attribute reduction for
the NSL-KDD dataset. The classification accuracies have
been compared with others algorithms and shown that the
proposed method can be one of the competitive
classifiers for IDS.",
-
keywords = "genetic algorithms, genetic programming",
-
DOI = "doi:10.1109/SNPD.2015.7176208",
-
month = jun,
-
notes = "Also known as \cite{7176208}",
- }
Genetic Programming entries for
Abdel-Rahman Hedar
Mohamed A Omer
Ahmed F Al-Sadek
Adel A Sewisy
Citations