DBSCAN Clustering Algorithm Based on Big Data Is Applied in Network Information Security Detection

Zhang, Yan

doi:10.1155/2022/9951609

Cited by 9 publications

(7 citation statements)

References 24 publications

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“…We decided to use DBSCAN because it has been used in multiple previous network intrusion detection works. [13][14][15][16][17][18]…”

Section: Dbscan Clusteringmentioning

confidence: 99%

Zero-shot learning for raw network traffic detection

Rani,

Bastian

2024

Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications VI

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In recognizing the importance of network traffic monitoring for cybersecurity, it is essential to acknowledge that most traditional machine learning models integrated in network intrusion detection systems encounter difficulty in training because acquiring labeled data involves an expensive and time-consuming process. This triggers an in-depth analysis into zero-shot learning techniques specifically designed for raw network traffic detection. Our innovative approach uses clustering combined with the instance-based method for zero-shot learning, enabling classification of network traffic without explicit training on labeled attack data and produces pseudo-labels for unlabeled data. This approach enables the development of accurate models with minimal limited labeled data for making network security more adaptable. Extensive computational experimentation is performed to evaluate our zero-shot learning approach using a real-world network traffic detection dataset. Finally, we offer insights into state-of-art developments and guiding efforts to enhance network security against ever-evolving cyber threats.

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“…We decided to use DBSCAN because it has been used in multiple previous network intrusion detection works. [13][14][15][16][17][18]…”

Section: Dbscan Clusteringmentioning

confidence: 99%

Zero-shot learning for raw network traffic detection

Rani,

Bastian

2024

Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications VI

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“…The specific procedure is shown as follows: Processing of datasets and calculation of statistics: Firstly, standardize D Then, Euclidean distance is usually used to calculate the distance between different samples of D, but for high-dimensional datasets using Equation (9). Then, the dichotomy is used to determine the truncation distance d c adaptively; the local density ρ i and the center deviation offset distance δ i are calculated, respectively, according to Equations ( 6) and ( 7).…”

Section: The Procedures Of Dp-cdpc Algorithmmentioning

confidence: 99%

“…2023, 13, 8674 2 of 20 to initialize the clustering center, and proposed an Empty-Circles-based K-means (ECKM) Clustering algorithm. In order to improve the certainty and clarity of information security detection, Y. Zhang [9] proposed a DBSCAN clustering algorithm based on big data. This algorithm was a method of applying a big data clustering algorithm to information security detection.…”

Section: Introductionmentioning

confidence: 99%

An Improved Density Peak Clustering Algorithm Based on Chebyshev Inequality and Differential Privacy

Chen,

Zhou,

Mei

et al. 2023

Applied Sciences

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This study aims to improve the quality of the clustering results of the density peak clustering (DPC) algorithm and address the privacy protection problem in the clustering analysis process. To achieve this, a DPC algorithm based on Chebyshev inequality and differential privacy (DP-CDPC) is proposed. Firstly, the distance matrix is calculated using cosine distance instead of Euclidean distance when dealing with high-dimensional datasets, and the truncation distance is automatically calculated using the dichotomy method. Secondly, to solve the difficulty in selecting suitable clustering centers in the DPC algorithm, statistical constraints are constructed from the perspective of the decision graph using Chebyshev inequality, and the selection of clustering centers is achieved by adjusting the constraint parameters. Finally, to address the privacy leakage problem in the cluster analysis, the Laplace mechanism is applied to introduce noise to the local density in the process of cluster analysis, enabling the privacy protection of the algorithm. The experimental results demonstrate that the DP-CDPC algorithm can effectively select the clustering centers, improve the quality of clustering results, and provide good privacy protection performance.

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“…DBSCAN algorithm can be applied on big data. Zhang (2022) applied DBSCAN in information security detection and also combine BIRCH with DBSCAN. Li, Yang, Jiao, and Li (2022) From some studies, the accuracy of K-Means and DBSCAN is depending on the data.…”

Section: Literature Reviewmentioning

confidence: 99%

Performance of K-Means and DBSCAN Algorithm in Clustering Gross Regional Domestic Product

Wororomi,

Girik Allo,

Paranoan

2023

JICP

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Gross Regional Domestic Product (GRDP) is one of important indicator to determine the economic conditions of a region. GRDP are obtained from sum of value added produced by all unit of production in a region. This study uses GRDP by production approach that grouped into seventeen categories of industry. The government always put the big efforts to increase the economic growth after Covid-19 pandemic. The aim of this study is determined the cluster GDRB based on province in Indonesia at current prices and analyses the performance of the cluster method. The results showed that by using the DBSCAN, two clusters were formed and one province can be detected as an outlier. On the other hands, performance of the method by K-Means showed two clusters. The silhouette value using K-Means is higher than the DBSCAN. For this case, the performance of K-Means is more appropriate than DBSCAN to use in clustering province in Indonesia based on GRDP at Current Market Prices. Moreover, performance of DBSCAN shows more sensitive on outliers detection.

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DBSCAN Clustering Algorithm Based on Big Data Is Applied in Network Information Security Detection

Cited by 9 publications

References 24 publications

Zero-shot learning for raw network traffic detection

Zero-shot learning for raw network traffic detection

An Improved Density Peak Clustering Algorithm Based on Chebyshev Inequality and Differential Privacy

Performance of K-Means and DBSCAN Algorithm in Clustering Gross Regional Domestic Product

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