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Bayesian Classifiers in Intrusion Detection Systems

Mardini-Bovea, Johan; De-La-Hoz-Franco, Emiro; Molina Estren, Diego; Ariza Colpas, Paola Patricia; Ortíz, Andrés; Ortega, Julio; R. Cárdenas, César A.; COLLAZOS MORALES, CARLOS ANDRES (Universidad de la Costa, 2020)

To be able to identify computer attacks, detection systems that are based on faults are not dependent on data base upgrades unlike the ones based on misuse. The first type of systems mentioned generate a knowledge pattern ...
Prediction of psychosocial risks in teachers using data mining

Viloria, Amelec; Rodríguez López, Jorge; Orellano Llinás, Nataly; Vargas Mercado, Carlos; León Coronado, Luz Estela; Negrete Sepulveda, Ana María; Pineda, Omar (Corporación Universidad de la Costa, 2020)

Integrated management systems aim to improve these everyday situations that are inherent to work and cause for concern. In search for continuous improvement, it is necessary to innovate with techniques in areas that are ...

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Bayesian Classifiers in Intrusion Detection Systems
...
Mardini-Bovea, Johan | 2020

To be able to identify computer attacks, detection systems that are based on faults are not dependent on data base upgrades unlike the ones based on misuse. The first type of systems mentioned generate a knowledge pattern from which the usual and unusual traffic is distinguished. Within computer networks, different classification traffic techniques have been implemented in intruder detection systems based on abnormalities. These try to improve the measurement that assess the performance quality of classifiers and reduce computational cost. In this research work, a comparative analysis of the obtained results is carried out after implementing different selection techniques such as Info.Gain, Gain ratio and Relief as well as Bayesian (Naïve Bayes and Bayesians Networks). Hence, 97.6% of right answers were got with 13 features. Likewise, through the implementation of both load balanced methods and attributes normalization and choice, it was also possible to diminish the number of features used in the ID classification process. Also, a reduced computational expense was achieved.

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Prediction of psychosocial risks in teachers using data mining
...
Viloria, Amelec | 2020

Integrated management systems aim to improve these everyday situations that are inherent to work and cause for concern. In search for continuous improvement, it is necessary to innovate with techniques in areas that are not yet explored and that contribute to strategic decision-making processes, such as machine learning techniques or machine learning. In occupational safety and health management systems, it is important to carry out the proper follow-ups and process controls in any type of industry and organization whose relationship is direct. This paper presents the application of three methods related to data mining: Support Vector Machine algorithms, Naïve Bayes, and Genetic Algorithms to identify the degree of psychosocial risk in university teachers of the Mumbai University in India. The use of SVM easily recognizes physiological variables and the best prediction performance was achieved with 96.34% accuracy efficiency.

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Listar por tema "Naïve bayes"

Bayesian Classifiers in Intrusion Detection Systems ﻿

Prediction of psychosocial risks in teachers using data mining ﻿

Envíos recientes

Bayesian Classifiers in Intrusion Detection Systems

Prediction of psychosocial risks in teachers using data mining

Bayesian Classifiers in Intrusion Detection Systems

Prediction of psychosocial risks in teachers using data mining