9786200326164 - phishing website detection using machine learning algorithms: a comparative analysis of phishing websites detection using xgboost algorithm with other machine learning algorithms de musa, hajara (5 resultados)
- Tapa blanda
Librería: preigu, Osnabrück, Alemaniapreigu
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 34,55
Envío por EUR 70,00Se envía de Alemania a Estados Unidos de AmericaCantidad disponible: 5 disponibles
Taschenbuch. Condición: Neu. Phishing Website Detection Using Machine Learning Algorithms | A Comparative Analysis of Phishing Websites Detection using XGBOOST Algorithm with other Machine Learning Algorithms | Hajara Musa | Taschenbuch | 84 S. | Englisch | 2019 | LAP LAMBERT Academic Publishing | EAN 9786200326164 | Verantwortl…iche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.
- Tapa blanda
- Impresión bajo demanda
Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, AlemaniaBuchWeltWeit Ludwig Meier e.K.
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 39,90
Envío por EUR 23,00Se envía de Alemania a Estados Unidos de AmericaCantidad disponible: 2 disponibles
Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Reducing the risk pose by phishers and other cyber criminals in the cyber space requires a robust and automatic means of detecting phishing websites, since the culprits are constantly coming up with new techniques of achieving their… goals almost on daily basis. Phishers are constantly evolving the methods they used for luring user to revealing their sensitive information. Many methods have been proposed in past for phishing detection. But the quest for better solution is still on. This research covers the development of phishing website model based on different algorithms with different set of features. The evaluation criteria are used in measuring the performance of phishing detection. Benchmark phishing website dataset were considered in the experiment. The result of the experiments showed that XGBOOST is better in most of the problems than the other methods in terms of the F.score, MCC, and Accuracy Therefore, the proposed method represents a very competitive for phishing detection. XGBOOST has a better regularization ability which helps to reduce overfitting, high speed and flexibility due to it costume optimization objectives and evaluation criteria. 84 pp. Englisch.
- Tapa blanda
- Impresión bajo demanda
Librería: moluna, Greven, Alemaniamoluna
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 34,25
Envío por EUR 48,99Se envía de Alemania a Estados Unidos de AmericaCantidad disponible: Más de 20 disponibles
Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Musa HajaraI am computer scientist at Gombe State University Nigeria, faculty of Science, Department of Mathematics, Computer program Unit I attended many conferences, workshops which I published many p…apers in machine learning, and.
- Tapa blanda
- Impresión bajo demanda
Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemaniabuchversandmimpf2000
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 39,90
Envío por EUR 60,00Se envía de Alemania a Estados Unidos de AmericaCantidad disponible: 1 disponibles
Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Reducing the risk pose by phishers and other cyber criminals in the cyber space requires a robust and automatic means of detecting phishing websites, since the culprits are constantly coming up with new techniques of achieving their goa…ls almost on daily basis. Phishers are constantly evolving the methods they used for luring user to revealing their sensitive information. Many methods have been proposed in past for phishing detection. But the quest for better solution is still on. This research covers the development of phishing website model based on different algorithms with different set of features. The evaluation criteria are used in measuring the performance of phishing detection. Benchmark phishing website dataset were considered in the experiment. The result of the experiments showed that XGBOOST is better in most of the problems than the other methods in terms of the F.score, MCC, and Accuracy Therefore, the proposed method represents a very competitive for phishing detection. XGBOOST has a better regularization ability which helps to reduce overfitting, high speed and flexibility due to it costume optimization objectives and evaluation criteria.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 84 pp. Englisch.
- Tapa blanda
- Impresión bajo demanda
Librería: AHA-BUCH GmbH, Einbeck, AlemaniaAHA-BUCH GmbH
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 40,89
Envío por EUR 60,72Se envía de Alemania a Estados Unidos de AmericaCantidad disponible: 1 disponibles
Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Reducing the risk pose by phishers and other cyber criminals in the cyber space requires a robust and automatic means of detecting phishing websites, since the culprits are constantly coming up with new techniques of achieving their goal…s almost on daily basis. Phishers are constantly evolving the methods they used for luring user to revealing their sensitive information. Many methods have been proposed in past for phishing detection. But the quest for better solution is still on. This research covers the development of phishing website model based on different algorithms with different set of features. The evaluation criteria are used in measuring the performance of phishing detection. Benchmark phishing website dataset were considered in the experiment. The result of the experiments showed that XGBOOST is better in most of the problems than the other methods in terms of the F.score, MCC, and Accuracy Therefore, the proposed method represents a very competitive for phishing detection. XGBOOST has a better regularization ability which helps to reduce overfitting, high speed and flexibility due to it costume optimization objectives and evaluation criteria.
