Modification of a Density-Based Spatial Clustering Algorithm for Applications with Noise for Data Reduction in Intrusion Detection Systems

Wiharto, Wiharto; Wicaksana, Aditya Kharisma; Cahyani, Denis Eka

doi:10.5391/ijfis.2021.21.2.189

Cited by 9 publications

(5 citation statements)

References 39 publications

(62 reference statements)

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“…Where the variables sensitivity (Sen) and Accuracy (Acc) are performance parameters with a formula as shown in equation (2)(3)(4). These parameters refer to the (2)…”

Section: Methodsmentioning

confidence: 99%

“…The last stage is the measurement of the performance of the proposed model. Performance measurement uses the parameters of accuracy, sensitivity, and precision (positive prediction value) with the formula shown in equation (2)(3)(4). In addition to these three parameters, performance parameters are also measured which are sensitivity and 1-specificity which are expressed in the area under the curve (AUC) parameter.…”

Section: Figure 2 Model Of Chromosomementioning

confidence: 99%

“…The intelligence system model using data mining techniques is divided into a number of stages, one of which is dimensional reduction. Dimensional reduction is divided into two, namely the reduction of the amount of data and the reduction of the number of attributes [2], [3]. The focus of many studies is the reduction of the number of attributes, known as feature selection.…”

Section: Introductionmentioning

confidence: 99%

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The Cost-Based Feature Selection Model for Coronary Heart Disease Diagnosis System Using Deep Neural Network

et al. 2022

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The development of feature selection models in intelligence systems for the diagnosis of coronary heart disease has been widely carried out. One of the developments that have been carried out is to minimize the number of inspections carried out. Unfortunately, many features selection models do not consider the cost of inspection, so the result of feature selection is an average inspection that requires high costs. This study proposes an intelligence system model for the diagnosis of coronary heart disease using a feature selection model that considers the cost of the examination. Feature selection is developed using a genetic algorithm and support vector machine. Decision-making of the diagnosis system is carried out using a deep neural network, with system performance being measured using the parameters of accuracy, sensitivity, positive predictive value, and area under the curve (AUC). The test results use the z-Alizadeh sani model feature selection dataset which produces 5 features out of 54 existing features. The use of these 5 features is able to produce AUC performance of 93.7%, accuracy of 87.7%, and sensitivity of 87.7%. Referring to the resulting performance, it shows that the feature selection model by considering the cost of an inspection is able to provide performance in the very good category.

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“…Where the variables sensitivity (Sen) and Accuracy (Acc) are performance parameters with a formula as shown in equation (2)(3)(4). These parameters refer to the (2)…”

Section: Methodsmentioning

confidence: 99%

Section: Figure 2 Model Of Chromosomementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The Cost-Based Feature Selection Model for Coronary Heart Disease Diagnosis System Using Deep Neural Network

et al. 2022

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“…Wiharto, W. and other scholars improved the PAM (partitioning around medoid, PAM) algorithm by using the interval number combined with the standard deviation of the data, proposed U-PAM (uncertain partitioning around medoid, U-PAM) algorithm, UM-PAM algorithm, and in order to determine the optimal number of clusters to make the clustering accuracy better, the algorithm introduces the CH index to provide guarantee, in order to a certain extent, the clustering accuracy has been improved. However, they still have the disadvantage of not being able to find clusters of arbitrary shapes [12]. Smith, A. J. and other scholars proposed the IUK-means algorithm, which is an uncertain data clustering algorithm based on fast Gaussian transformation; in the similarity measurement, the attribute characteristics of the uncertainty data are combined with their probability density functions, the similarity between objects is more accurately measured to complete the clustering.…”

Section: Related Workmentioning

confidence: 99%

Initial Clustering Based on the Swarm Intelligence Algorithm for Computing a Data Density Parameter

Xiong¹

2022

Computational Intelligence and Neuroscience

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To improve the accuracy and efficiency of cluster startup using data density parameters, the author proposes a large data cluster extraction algorithm based on a herd intelligence algorithm. Since clustering to initiate data density parameters is primarily data mining, the author explores data mining clustering based primarily on herd intelligence algorithms. First, the obscure c-key cluster algorithm in the clustering algorithm is analyzed, and then the hybrid jump algorithm in the sub-heuristic herd intelligence optimization technology is optimized in the case of a few parameters by combining the obscure c-means cluster algorithm. The simulation results show that the convergence speed of the fuzzy C-means clustering algorithm and hybrid leapfrog algorithm is slow; the convergence rate of the PSO-FCM algorithm has been improved. Since the fusion algorithm requires fewer adjustment parameters, the cluster centers can be obtained more accurately and quickly with strong robustness and fast convergence. Compared with other algorithms, the fusion algorithm proposed by the author has the best performance in clustering effect, accuracy, convergence rate, and robustness. It is proved that the swarm intelligence algorithm can effectively perform density parameter initialization clustering on computational data.

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“…Data stream learning is divided into two main types: supervised and unsupervised learning. Clustering is an unsupervised learning method [12].…”

Section: Related Workmentioning

confidence: 99%

An Evolving Fuzzy Model to Determine an Optimal Number of Data Stream Clusters

Al-Khamees¹,

Al-A’araji²,

Al-Shamery³

2022

IJFIS

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Data streams are a modern type of data that differ from traditional data in various characteristics: their indefinite size, high access, and concept drift due to their origin in non-stationary environments. Data stream clustering aims to split these data samples into significant clusters, depending on their similarity. The main drawback of data stream clustering algorithms is the large number of clusters they produce. Therefore, determining an optimal number of clusters is an important challenge for these algorithms. In practice, evolving models can change their general structure by implementing different mechanisms. This paper presents a fuzzy model that mainly consists of an evolving Cauchy clustering algorithm which is updated through a specific membership function and determines the optimal number of clusters by implementing two evolving mechanisms: adding and splitting clusters. The proposed model was tested on six different streaming datasets, namely, power supply, sensor, HuGaDB, UCI-HAR, Luxembourg, and keystrokes. The results demonstrated that the efficiency of the proposed model in producing an optimal number of clusters for each dataset outperforms that of previous models.

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Modification of a Density-Based Spatial Clustering Algorithm for Applications with Noise for Data Reduction in Intrusion Detection Systems

Cited by 9 publications

References 39 publications

The Cost-Based Feature Selection Model for Coronary Heart Disease Diagnosis System Using Deep Neural Network

The Cost-Based Feature Selection Model for Coronary Heart Disease Diagnosis System Using Deep Neural Network

Initial Clustering Based on the Swarm Intelligence Algorithm for Computing a Data Density Parameter

An Evolving Fuzzy Model to Determine an Optimal Number of Data Stream Clusters

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