Abstract
Phishing attacks cause people to be scammed and cheated because of the impossibility to visually detect fraudulent websites. As is known, the attack occurs from emails sent to collect or update information supposedly from an entity, there are also cases of phone calls or instant messages. There is ignorance of such attacks by people in general, which means that the user is not alerted, which means that he is not attentive to the digital certificates present on the page that authenticate the content of the same. For this reason, the web pages designed have required tools that counteract and alert the user of the “phished” webpages, which commit the theft of money from the account from which information has been provided.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Baykara, M., Grürel, Z.: Detection of phishing attacks. In: 6th International Symposium on Digital Forensic and Security (ISDFS), Antalya (2018)
Karabatak, M., Mustafa, T.: Performance comparation of classifiers on reduced phishing website dataset. In: 6th International Symposium on Digital Forensic and Security (ISDFS), Antalya (2018)
Fadheel, W., Abusharkh, M., Abdel-Qader, I.: On Feature selection for the prediction of phishing websites. In: 15th International Conference on Dependable, Autonomic and Secure Computing, 15th International Conference on Pervasive Intelligence and Computing, Orlando (2017)
Shaikh, A., Shabut, A., Hossain, M.: A literature review on phishing crime, prevention review and investigation of gaps. In: 10th International Conference on Software, Knowledge, Information Management and Applications, Chengdu (2016)
Gaonkar, M.N., Sawant, K.: AutoEpsDBSCAN: DBSCAN with Eps automatic for large dataset. Int. J. Adv. Comput. Theor. Eng. 2319–2526 (2013)
Ester, M.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining, Oregon (1996)
Gupta, B., Nalin, A., Psannis, K.: Defending against phishing attacks: taxonomy of methods, current issues and future directions. Telecommun. Syst. Model. Anal. Des. Manag. 67, 247–267 (2018)
Gupta, B., Tewari, A., Jain, A., Agrawal, D.: Fighting against phishing attacks: state of the art and future challenges. Nat. Comput. Appl. Forum 1–26 (2016)
Abbasi, A., Zahedi, F.M., Chen, Y.: Impact of anti-phishing tool performance on attack success rates. In: 10th IEEE International Conference on Intelligence and Security Informatics (ISI), Washington (2012)
Mockford, A.: An exploratory descriptive study of the needs of parents after their young child is discharged from hospital following an admission with an acute illness, Victoria University of Wellington, Wellington (2008)
Rogalewicz, M., Sika, R.: Methodologies of knowledge discovery from data and data mining methods in mechanical engineering. Manag. Prod. Eng. Rev. 7(4), 97–108 (2016)
Hurwitz, J., Kirsch, D.: Machine Learning for Dummies. Wiley, New York (2018)
Berthold, M., Hand, D.: Intelligence Data Analysis - An Introduction. Springer, New York (2002)
Mesa, O. Rivera, M., Romero, J.: Descripción general de la Inferencia Bayesiana y sus aplicaciones en los procesos de gestión, La simulación al servicio de la academia, vol. 2, pp. 1–3 (2011)
Mitchell, T.: Machine Learning. McGraw-Hill, United States of America (1997)
Chai, K., Hn, H.T., Chieu, H.L.: Bayesian online classifiers for text classification and filtering. In: 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Tampere (2002)
Witten, I., Eibe, F.: Data Mining: Practical Machine Learning Tools and Techniques with Java Impletations. Morgan Kaufmann, San Diego (2000)
Hellerstein, J., Thathachar, J., Rish, I.: Recognizing end-user transactions in performance menagement. In: Proceedings of AAAI-2000, pp. 596–602 (2000)
González O, F.A.: Diplomado en Inteligencia de Necogios - módulo minería de datos, Universidad Nacional de Colombia. http://dis.unal.edu.co/~fgonza/courses/2007-II/mineria/bayesianos.pdf
Taheri, S., Mammadov, M.: Learning The Naive Bayes Classifier With Optimization Models. Int. J. Appl. Math. Comput. Sci. (2013)
Hernández Orallo, J., Ramírez Quintana, M., Ferri Ramírez, C.: Introducción a la minería de datos, Pearson (2004)
Guyon, I., Elissee, A.: An introduction to variable and feature selection. J. Mach. Learn. Res. 3, 1157–1182 (2003)
Rodriguez, J.E., Medina, V.H., Ospina, M.A.: Corporate networks traffic analysis for knowledge management based on random interactions clustering algorithm. J. Commun. Comput. Inf. Sci. 877(1), 523–536 (2018)
Shah, G.: An improved DBSCAN, a density based clustering algorithm with parameter selection for high dimensional datasets. In: Nirma University International Conference on Engineering, Gujarat (2012)
John, G., Langley, P.: Estimating continuous distributions in Bayesian classifiers. In: Eleventh Conference on Uncertainty in Artificial Intelligence, pp. 338–345. Morgan Kaufmann Publisher (1995)
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Rodríguez, J.E.R., García, V.H.M., Castillo, N.P. (2019). Webpages Classification with Phishing Content Using Naive Bayes Algorithm. In: Uden, L., Ting, IH., Corchado, J. (eds) Knowledge Management in Organizations. KMO 2019. Communications in Computer and Information Science, vol 1027. Springer, Cham. https://doi.org/10.1007/978-3-030-21451-7_21
Download citation
DOI: https://doi.org/10.1007/978-3-030-21451-7_21
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-21450-0
Online ISBN: 978-3-030-21451-7
eBook Packages: Computer ScienceComputer Science (R0)