A Framework for Selecting Classification Models in the Intruder Detection System Using TOPSIS

Martínez, Miguel Ángel Quiroz; Rugel, Deivid Temistocles Leon; Alcivar, Carlos Jose Espinoza; Vázquez, Maikel Yelandi Leyva

doi:10.1007/978-3-030-55307-4_27

Cited by 2 publications

(2 citation statements)

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“…TOPSIS 23 is an MCDM algorithmic model which was proposed by Hwang and Yoon in 1981, and has been used widely in various domains which require complex decision making process. TOPSIS is preferred over other approaches because of its suitability for large number of attributes and comparative consistency in the alternative ranking.…”

Section: Resultsmentioning

confidence: 99%

“…We underwent a comparative study of this proposed framework with the other works 9,22,23,25 performed based on IDS framework establishment which had used NSL-KDD, UNSE-NB15 as the datasets. The performance is evaluated in the following aspects: usage of statistical model for classifier selection, adaptive, single or ensemble, whether feature selection is applied, number of features selected, type of datasets used, accuracy and FPR, which is shown in Table 20.…”

Section: Topsis Based Classifier Selectionmentioning

confidence: 99%

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Context adaptive ensemble classification mechanism with multi‐criteria decision making for network intrusion detection

Devprasad¹,

Ramanujam

Rajendran³

2022

Concurrency and Computation

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Nowadays, intrusion detection systems (IDSs) enabled with computational intelligence for electing the suitable classifiers to learn the patterns of various types of network attacks are under study. Selection mechanism of single or ensemble classifiers based on performance and ranking is to be enabled in the IDS framework to make accurate pattern predictions. A new anomaly based network intrusion detection system framework is proposed using context adaptive classification through the technique for order of preference by similarity to ideal solution (TOPSIS) ranking mechanism with hierarchy based chi-square and bat algorithms for feature selection.Parameters like accuracy, false positive rate (FPR), and classification model building time are explored for choosing the best and situation aware classifiers using TOPSIS.For experimentation, NSL-KDD and UNSW-NB15 datasets are used. The proposed system opts decision tree (DT) algorithm for NSL-KDD and produces 98.77% accuracy, 0.03% FPR with 8 features. Ensemble learner is selected for the UNSW-NB15 dataset using DT and support vector machine classifiers with 9 features. Combined classifier predicts 89.43% accuracy and 3.215% FPR. Experimentation on the proposed methodology with the state of the art approaches produces promising results.

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Section: Resultsmentioning

confidence: 99%