Hybrid evolutionary algorithms for data classification in intrusion detection systems

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@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