Inconsistency Measure Associated Discretization Methods to Network-based Intrusion Detection

Zhao, Rong; Qu, Yanpeng; Deng, Ansheng; Zwiggelaar, Reyer

doi:10.1109/fuzz-ieee.2018.8491570

Cited by 3 publications

(3 citation statements)

References 24 publications

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“…In classification, discretization process is employed in data processing step before performing the feature selection process. Data preparation is one of the critical steps in classification problems [16]. The results from preparation is directly incorporated into the model and final results will be obtained.…”

Section: Discretizationmentioning

confidence: 99%

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A New Discretization Approach of Bat and K-Means

Mohamed¹,

Samsudin²

2021

IJACSA

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Bat algorithm is one of the optimization techniques that mimic the behavior of bat. Bat algorithm is a powerful algorithm in finding the optimum feature data collection. Classification is one of the data mining tasks that useful in knowledge representation. But, the high dimensional data become the issue in the classification that interrupt classification accuracy. From the literature, feature selection and discretization able to overcome the problem. Therefore, this study aims to show Bat algorithm is potential as a discretization approach and as a feature selection to improve classification accuracy. In this paper, a new hybrid Bat-K-Mean algorithm refer as hBA is proposed to convert continuous data into discrete data called as optimize discrete dataset. Then, Bat is used as feature selection to select the optimum feature from the optimized discrete dataset in order to reduce the dimension of data. The experiment is conducted by using k-Nearest Neighbor to evaluate the effectiveness of discretization and feature selection in classification by comparing with continuous dataset without feature selection, discrete dataset without feature selection, and continuous dataset without discretization and feature selection. Also, to show Bat is potential as a discretization approach and feature selection method. . The experiments were carried out using a number of benchmark datasets from the UCI machine learning repository. The results show the classification accuracy is improved with the Bat-K-Means optimized discretization and Bat optimized feature selection.

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Section: Discretizationmentioning

confidence: 99%

“…K-Means was describe by J. MacQueen in 1967 is an iterative algorithms [20]. The K-Means is used as the discretization algorithm [21]. The standard K-Means algorithm as shows in Fig.…”

Section: Discretizationmentioning

confidence: 99%

A New Discretization Approach of Bat and K-Means

Mohamed¹,

Samsudin²

2021

IJACSA

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“…In another research, K-Means is combined with discretization technique and Naïve Bayes classifier (Tahir et al, 2016) applied in network intrusion detection system. Moreover, K-Means can be implemented as discretization approach without combination with another approach such as in network intrusion detection research (Zhao et al, 2018)and graph optimal graph clustering (Han et al, 2020)…”

Section: Literature Review a K-means As Discretization Approachmentioning

confidence: 99%

An Optimized DiscretizationApproach using k-Means Bat Algorithm

et.al¹

2021

TURCOMAT

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This study has proposed arelatively new discretization approach using k-means and Bat algorithm in preparation phase of classification problem. In essence, bat algorithm is applied to find the best search space solution. Eventually, the best search space solution is utilized to produce cluster centroid. The cluster centroid is very useful to determine appropriate breakpoint for discretization. The proposed discretization approach is applied in the experiments with continuous datasets. Decision Tree, k-Nearest Neighbours and Naïve Bayes classifiers are used in the experiments. The proposed discretization approach is evaluated against other existing approaches: K-Means algorithm, hybrid K-Means with Particle Swarm Optimization (PSO) and hybrid K-Means with Whale Optimization Algorithm (WOA).The classification performance is evaluated in terms of accuracy, recall, f-measure and receiver operating characteristic curve (ROC). To test the performance of the proposed algorithm, nine benchmark continuous datasets are used. The proposed algorithm show the better results compare to other approaches. The proposed algorithm performs better in discretization to solve classification problems.

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Radar Point Cloud Clustering Method Based on Optimization

Shen

Zhu

et al. 2021

2021 IEEE 4th International Conference on Computer and Communication Engineering Technology (CCET)

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Inconsistency Measure Associated Discretization Methods to Network-based Intrusion Detection

Cited by 3 publications

References 24 publications

A New Discretization Approach of Bat and K-Means

A New Discretization Approach of Bat and K-Means

An Optimized DiscretizationApproach using k-Means Bat Algorithm

Radar Point Cloud Clustering Method Based on Optimization

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