Mixing numerical and categorical data in a Self-Organizing Map by means of frequency neurons

Coso, Carmelo del; Fustes, Diego; Dafonte, Carlos; Nóvoa, Francisco J.; Rodríguez-Pedreira, José M.; Arcay, B.

doi:10.1016/j.asoc.2015.06.058

Cited by 20 publications

(3 citation statements)

References 9 publications

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“…For the scope of this work, to generate the clusters, we have modified SOM technique to accept numerical and categorical features, as explained in [11]. Besides we have only used information present on IP packet headers or values derived from them.…”

Section: Methodsmentioning

confidence: 99%

Network Data Unsupervised Clustering to Anomaly Detection

López-Vizcaíno¹,

Dafonte²,

Nóvoa³

et al. 2018

XoveTIC Congress 2018

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In these days, organizations rely on the availability and security of their communication networks to perform daily operations. As a result, network data must be analyzed in order to provide an adequate level of security and to detect anomalies or malfunctions in the systems. Due to the increase of devices connected to these networks, the complexity to analyze data related to its communications also grows. We propose a method, based on Self-Organized Maps, which combine numerical and categorical features, to ease communication network data analysis. Also, we have explored the possibility of using different sources of data.

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Section: Methodsmentioning

confidence: 99%

Network Data Unsupervised Clustering to Anomaly Detection

López-Vizcaíno¹,

Dafonte²,

Nóvoa³

et al. 2018

XoveTIC Congress 2018

Self Cite

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“…Moreover, we not only show the retrieved data, but also process them in order to generate a dataset on which unsupervised machine learning techniques can be applied. Concretely, we use data involving services and protocols to train a self-organizing map (SOM) [4] that clusterizes our samples, providing an easy-to-understand and very visual way to distinguish the devices in our network, which are atypical when it comes to the services running or protocols used.…”

Section: Architectural Designmentioning

confidence: 99%

Low Cost Automated Security Audit System

Fernández-Arruti¹,

Estévez-Pereira²,

Nóvoa³

et al. 2021

The 4th XoveTIC Conference

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In recent years, a quick transition towards digitization has been observed in most organizations. Along with it, certain inherent problems have appeared, such as the increase in cyber threats. Large organizations are able to adapt easily, but this does not happen with small and medium-sized companies. Currently, there are very few solutions aimed at fulfilling the needs of these small enterprises, so we have worked on a tool for them. Our tool is capable of displaying key, easy-to-interpret information related to each organization’s network assets. To achieve this, we used passive and active analysis techniques and successfully evaluated the viability of using machine learning techniques to get more meaningful information. All of the information obtained is displayed in a simple web application, which is designed to be used by managers in organizations without them needing to handle complex concepts and vocabulary.

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“…We decided to develop and implement a Web application, working with Apache Tapestry because of its potential and, to provide useful features for plots and statistics, JavaScript, 3js and SVG libraries were really helpful. We also integrated SAMP [2] (Simple Application Messaging Protocol) to allow data exchange with other Gaia tools, as described below.…”

Section: Classification Toolmentioning

confidence: 99%

Analysis and Knowledge Discovery by Means of Self-Organizing Maps for Gaia Data Releases

Álvarez

Dafonte

Garabato

et al. 2016

Neural Information Processing

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A billion stars: this is the approximate amount of visible objects estimated to be observed by the Gaia satellite, representing roughly 1% of the objects in the Galaxy. It constitutes the biggest amount of data gathered to date: by the end of the mission, the data archive will exceed 1 Petabyte. Now, in order to process this data, the Gaia mission conceived the Data Processing and Analysis Consortium, which will apply data mining techniques such as Self-Organizing Maps. This paper shows an useful technique for source clustering, focusing on the development of an advanced visualization tool based on this technique.

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Mixing numerical and categorical data in a Self-Organizing Map by means of frequency neurons

Cited by 20 publications

References 9 publications

Network Data Unsupervised Clustering to Anomaly Detection

Network Data Unsupervised Clustering to Anomaly Detection

Low Cost Automated Security Audit System

Analysis and Knowledge Discovery by Means of Self-Organizing Maps for Gaia Data Releases

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