Density based clustering technique for efficient data mining

Rahman, Aminur; Chowdhury, Abdur; Rahman, D.M.J.; Kamal, Abu Raihan Mostofa

doi:10.1109/iccitechn.2008.4803050

Cited by 4 publications

(3 citation statements)

References 7 publications

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“…Then the issue of dataset size is addressed by using feature selection to reduce the Kyoto 2006+ dataset from 24 to nine numerical features recognized as the most relevant for classifier evaluation. The literature implies that users who are knowledgeable about their dataset can select features that meet some criteria based on their knowledge and experience [49,50]. According to this principle, feature selection proposed in this paper is performed as follows: (1) Remove all categorical features (17 features are left for model training: 1, 3-17, 24), ( 2) Cut out all statistical features and features for more analyses planned.…”

Section: Resultsmentioning

confidence: 99%

XOR-Based Detector of Different Decisions on Anomalies in the Computer Network Traffic

PROTIC,

STANKOVIC

2023

ROMJIST

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Anomaly-based intrusion detection systems are designed to scan computer network traffic for abnormal behavior. Binary classifiers based on supervised machine learning have proven to be highly accurate tools for classifying instances as normal or abnormal. Main disadvantages of supervised machine learning are the long processing time and large amount of training data required to ensure accurate results. Two preprocessing steps to reduce data sets are feature selection and feature scaling. In this article, we present a new hyperbolic tangent feature scaling approach based on the linearization of the tangent hyperbolic function and the damping strategy of the Levenberg-Marquardt algorithm. Experiments performed on the Kyoto 2006+ dataset used four high-precision binary classifiers: weighted k-nearest neighbors, decision tree, feedforward neural networks, and support vector machine. It is shown that hyperbolic tangent scaling reduces processing time by more than twofold. An XOR-based detector is proposed to determine conflicting decisions about anomalies. The decisions of the FNN and wk-NN models are compared. It is shown that decisions sometimes turn out differently. The percentage of the opposite decisions has been shown to vary and is not affected by dataset size.

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Section: Resultsmentioning

confidence: 99%

XOR-Based Detector of Different Decisions on Anomalies in the Computer Network Traffic

PROTIC,

STANKOVIC

2023

ROMJIST

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“…Table 4 shows a review of the research topics from 2018 to 2022 [12,24,85,91,[96][97][98][99][100][101][102]106]. According to the findings in [107,108], users who gain knowledge about the datasets can choose features that meet specific criteria as a result of their experience.…”

Section: Binary Classificationmentioning

confidence: 99%

“…Table 4 show view of the research topics from 2018 to 2022 [12,24,85,91,[96][97][98][99][100][101][102]106]. According findings in [107,108], users who gain knowledge about the datasets can choose f that meet specific criteria as a result of their experience. Likewise, the feature selection is performed here to eliminate statistical, connection duration and categorical features from the Kyoto 2006+ dataset, along with the features intended to be used in further experiments.…”

Section: Binary Classificationmentioning

confidence: 99%

Numerical Feature Selection and Hyperbolic Tangent Feature Scaling in Machine Learning-Based Detection of Anomalies in the Computer Network Behavior

Protić,

Stanković,

Prodanović

et al. 2023

Electronics

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Anomaly-based intrusion detection systems identify the computer network behavior which deviates from the statistical model of typical network behavior. Binary classifiers based on supervised machine learning are very accurate at classifying network data into two categories: normal traffic and anomalous activity. Most problems with supervised learning are related to the large amount of data required to train the classifiers. Feature selection can be used to reduce datasets. The goal of feature selection is to select a subset of relevant input features to optimize the evaluation and improve performance of a given classifier. Feature scaling normalizes all features to the same range, preventing the large size of features from affecting classification models or other features. The most commonly used supervised machine learning models, including decision trees, support vector machine, k-nearest neighbors, weighted k-nearest neighbors and feedforward neural network, can all be improved by using feature selection and feature scaling. This paper introduces a new feature scaling technique based on a hyperbolic tangent function and damping strategy of the Levenberg–Marquardt algorithm.

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Recursive density-based hierarchical clustering in gaussian distributed sensor network

Gupta

Sinha

2020

Int J Syst Assur Eng Manag

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Density based clustering technique for efficient data mining

Cited by 4 publications

References 7 publications

XOR-Based Detector of Different Decisions on Anomalies in the Computer Network Traffic

XOR-Based Detector of Different Decisions on Anomalies in the Computer Network Traffic

Numerical Feature Selection and Hyperbolic Tangent Feature Scaling in Machine Learning-Based Detection of Anomalies in the Computer Network Behavior

Recursive density-based hierarchical clustering in gaussian distributed sensor network

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