Article

GP ensemble for distributed intrusion detection systems

Authors:

Gianluigi Folino,

Clara Pizzuti,

Giandomenico SpezzanoAuthors Info & Claims

ICAPR'05: Proceedings of the Third international conference on Advances in Pattern Recognition - Volume Part I

Pages 54 - 62

https://doi.org/10.1007/11551188_6

Published: 22 August 2005 Publication History

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Abstract

In this paper an intrusion detection algorithm based on GP ensembles is proposed. The algorithm runs on a distributed hybrid multi-island model-based environment to monitor security-related activity within a network. Each island contains a cellular genetic program whose aim is to generate a decision-tree predictor, trained on the local data stored in the node. Every genetic program operates cooperatively, yet independently by the others, by taking advantage of the cellular model to exchange the outmost individuals of the population. After the classifiers are computed, they are collected to form the GP ensemble. Experiments on the KDD Cup 1999 Data show the validity of the approach.

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Cited By

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Picek SJakobovic DWagner M(2022)Evolutionary computation and machine learning in securityProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3520304.3534087(1572-1601)Online publication date: 9-Jul-2022
https://dl.acm.org/doi/10.1145/3520304.3534087
Picek SJakobovic DKrawiec K(2021)Evolutionary computation and machine learning in cryptologyProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3449726.3461420(1089-1118)Online publication date: 7-Jul-2021
https://dl.acm.org/doi/10.1145/3449726.3461420
Martí LSchoenauer MTakadama K(2018)Bio-inspired approaches to anomaly and intrusion detectionProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3205651.3207853(1121-1137)Online publication date: 6-Jul-2018
https://dl.acm.org/doi/10.1145/3205651.3207853
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GP ensemble for distributed intrusion detection systems
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
    2. Machine learning approaches

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Information

Published In

ICAPR'05: Proceedings of the Third international conference on Advances in Pattern Recognition - Volume Part I

August 2005

688 pages

ISBN:3540287574

Editors:
Sameer Singh
Research School of Infomatics, Loughborough, UK
,
Maneesha Singh
ATR Lab, Research School of Informatics, University of Loughborough, Loughborough, UK
,
Chid Apte
ATR Lab, Research School of Informatics, IBM Corporation, 1133 Wetchester Avenue, White Plains, New York, United States
,
Petra Perner
Institute of Computer Vision and applied Computer Sciences, IBaI, 1133 Wetchester Avenue, White Plains, New York, Germany

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 22 August 2005

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Picek SJakobovic DWagner M(2022)Evolutionary computation and machine learning in securityProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3520304.3534087(1572-1601)Online publication date: 9-Jul-2022
https://dl.acm.org/doi/10.1145/3520304.3534087
Picek SJakobovic DKrawiec K(2021)Evolutionary computation and machine learning in cryptologyProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3449726.3461420(1089-1118)Online publication date: 7-Jul-2021
https://dl.acm.org/doi/10.1145/3449726.3461420
Martí LSchoenauer MTakadama K(2018)Bio-inspired approaches to anomaly and intrusion detectionProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3205651.3207853(1121-1137)Online publication date: 6-Jul-2018
https://dl.acm.org/doi/10.1145/3205651.3207853
źUrasević MJakobović D(2018)Comparison of ensemble learning methods for creating ensembles of dispatching rules for the unrelated machines environmentGenetic Programming and Evolvable Machines10.1007/s10710-017-9302-319:1-2(53-92)Online publication date: 1-Jun-2018
https://dl.acm.org/doi/10.1007/s10710-017-9302-3
Khasawneh KOzsoy MDonovick CAbu-Ghazaleh NPonomarev D(2015)Ensemble Learning for Low-Level Hardware-Supported Malware DetectionProceedings of the 18th International Symposium on Research in Attacks, Intrusions, and Defenses - Volume 940410.1007/978-3-319-26362-5_1(3-25)Online publication date: 2-Nov-2015
https://dl.acm.org/doi/10.1007/978-3-319-26362-5_1
Mabu SHirasawa KObayashi MKuremoto T(2014)A variable size mechanism of distributed graph programs and its performance evaluation in agent control problemsExpert Systems with Applications: An International Journal10.1016/j.eswa.2013.08.06341:4(1663-1671)Online publication date: 1-Mar-2014
https://dl.acm.org/doi/10.1016/j.eswa.2013.08.063
Pastrana SMitrokotsa AOrfila APeris-Lopez P(2012)Evaluation of classification algorithms for intrusion detection in MANETsKnowledge-Based Systems10.1016/j.knosys.2012.06.01636(217-225)Online publication date: 1-Dec-2012
https://dl.acm.org/doi/10.1016/j.knosys.2012.06.016
Wu SBanzhaf W(2010)ReviewApplied Soft Computing10.1016/j.asoc.2009.06.01910:1(1-35)Online publication date: 1-Jan-2010
https://dl.acm.org/doi/10.1016/j.asoc.2009.06.019
Folino GPizzuti CSpezzano G(2010)An ensemble-based evolutionary framework for coping with distributed intrusion detectionGenetic Programming and Evolvable Machines10.1007/s10710-010-9101-611:2(131-146)Online publication date: 1-Jun-2010
https://dl.acm.org/doi/10.1007/s10710-010-9101-6
Tian DLiu YLi B(2007)A distributed hebb neural network for network anomaly detectionProceedings of the 5th international conference on Parallel and Distributed Processing and Applications10.5555/2395970.2396004(314-325)Online publication date: 29-Aug-2007
https://dl.acm.org/doi/10.5555/2395970.2396004
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A Survey on Intrusion Detection and Prevention Systems

An ensemble-based evolutionary framework for coping with distributed intrusion detection

Ensemble based collaborative and distributed intrusion detection systems

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