Hybrid evolutionary algorithms for data classification in intrusion detection systems

Hedar, Abdel-Rahman; Omer, Mohamed; Al-Sadek, Ahmed Farouk; Sewisy, Adel A.

doi:10.1109/snpd.2015.7176208

Cited by 7 publications

(2 citation statements)

References 10 publications

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“…Hedar et al [61] proposed a new hybrid IDS based on accelerated genetic algorithm and rough set theory for data feature reduction as well as genetic programming with local search for data classification. In particular, they identified that data feature reduction contributes to improve classification performance and reduce memory and CPU time.…”

Section: Mapping Selected Studies By Ensemble Methodsmentioning

confidence: 99%

Ensemble learning for intrusion detection systems: A systematic mapping study and cross-benchmark evaluation

Tama

Lim

2021

Computer Science Review

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Intrusion detection systems (IDSs) are intrinsically linked to a comprehensive solution of cyberattacks prevention instruments. To achieve a higher detection rate, the ability to design an improved detection framework is sought after, particularly when utilizing ensemble learners. Designing an ensemble often lies in two main challenges such as the choice of available base classifiers and combiner methods. This paper performs an overview of how ensemble learners are exploited in IDSs by means of systematic mapping study. We collected and analyzed 124 prominent publications from the existing literature. The selected publications were then mapped into several categories such as years of publications, publication venues, datasets used, ensemble methods, and IDS techniques. Furthermore, this study reports and analyzes an empirical investigation of a new classifier ensemble approach, called stack of ensemble (SoE) for anomaly-based IDS. The SoE is an ensemble classifier that adopts parallel architecture to combine three individual ensemble learners such as random forest, gradient boosting machine, and extreme gradient boosting machine in a homogeneous manner. The performance significance among classification algorithms is statistically examined in terms of their Matthews correlation coefficients, accuracies, false positive rates, and area under ROC curve metrics. Our study fills the gap in current literature concerning an up-to-date systematic mapping study, not to mention an extensive empirical evaluation of the recent advances of ensemble learning techniques applied to IDSs.

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Section: Mapping Selected Studies By Ensemble Methodsmentioning

confidence: 99%

Ensemble learning for intrusion detection systems: A systematic mapping study and cross-benchmark evaluation

Tama

Lim

2021

Computer Science Review

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“…Hedar vd. [34], bir sisteme izinsiz girişleri sınıflandırmak ve belirlemek için genetik algoritma, kaba set teorisi ve genetik programlamaya dayalı bir yöntem önermişlerdir. 25192 sinyal verisinin yanısıra UCI veri tabanından alınan sınıflandırma veri setlerine göre yapılan çalışmanın sonuçları doğru sınıflandırma oranları bakımından özellikle çok katmanlı sinir ağı ve destek vektör makinelerinden oldukça başarılı olduğunu ortaya koymaktadır.…”

Section: Gi̇ri̇ş (Introduction)unclassified

Çok gruplu sınıflandırma problemlerine regresyon analizi ve matematiksel programlama tabanlı yeni bir yaklaşım

Doğan¹,

Orman²,

Örkcü³

et al. 2019

Gazi Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi

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 A new approach for the multi group classification problems  Using the regression analysis for the obtaining the classification scores  Using the mathematical programming for the classifying of the units Classification is the problem of identifying to which of a set of categories a new observation belongs, on the basis of a training set of data containing observations whose category membership is known. In this study, for solving multi-group classification problems, a new two-stage hybrid classification method based on regression analysis and mathematical programming has been developed. In the first step of the proposed method, the classification score of each unit is estimated with the help of the linear regression equation for each unit. In the second step, the classification of the units is performed by the mathematical programming model based on clustering analysis. Figure A. Flow chart of proposed method Purpose: In this study, for solving multi-group classification problems, a new method has been proposed based on regression analysis and mathematical programming classification method. The purpose of this study is handled the multi-group classification problem with the help of the superiority of regression analysis from the statistical theory and the flexibility of mathematical programming by a two-stage detailed examination idea. Theory and Methods: The proposed method combines the strengths of regression analysis and mathematical programming method. Results: From the 10 real data sets taken the well-known literature and simulation study results, it is observed that the proposed method outperforms the regression analysis, mathematical programming and artificial neural network based classification methods. Conclusion:With the proposed method, it is possible to achieve high correct classification success in the multiclass classification problems.

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Survey on Hybrid Data Mining Algorithms for Intrusion Detection System

Datir¹,

Jawandhiya²

2018

Data Management, Analytics and Innovation

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Hybrid evolutionary algorithms for data classification in intrusion detection systems

Cited by 7 publications

References 10 publications

Ensemble learning for intrusion detection systems: A systematic mapping study and cross-benchmark evaluation

Ensemble learning for intrusion detection systems: A systematic mapping study and cross-benchmark evaluation

Çok gruplu sınıflandırma problemlerine regresyon analizi ve matematiksel programlama tabanlı yeni bir yaklaşım

Survey on Hybrid Data Mining Algorithms for Intrusion Detection System

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