A Novel Intelligent Ensemble Classifier for Network Intrusion Detection System

Jabbar, M. A.; Srinivas, K.; Reddy, S. Sai Satyanarayana

doi:10.1007/978-3-319-60618-7_48

Cited by 5 publications

(2 citation statements)

References 7 publications

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“…Zaman and Lung [109] presented six machine learning techniques (i.e., k-means, k-NN, fuzzy c-means, naïve Bayes, SVM, and radial basis function) and an ensemble method for network traffic anomaly detection. Jabbar et al [84] used naïve Bayes and ADTree for developing an novel ensemble classifier that is used for network IDSs. Kevric et al [76] proposed an effective combining classifier method using tree-based algorithms, such as random tree, C4.5, and NBTree, for network intrusion detection.…”

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

View full text Add to dashboard Cite

show abstract

“…For example in article [1] the author proposes to use ensemble classifier based on naïve Bayes and ADTree. Usage of ADTrees complements naïve Bayes approach, which assumes that all analyzed features are independent.…”

Section: Existing Approaches To Intrusion Detectionmentioning

confidence: 99%