Characterization of Phishing Attacks and Techniques to Mitigate These Attacks: A Systematic Review of The Literature
In Computer Security, it does not matter whichSoftware or Hardware equipment is installed, becausealways the weakest link in this security chain, is the enduser. From this premise are used the different types ofSocial Engineering attacks, whose main objective isto obtain information almost directly...
| Autores: | , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2020 |
| País: | Ecuador |
| Institución: | Universidad Técnica Estatal de Quevedo |
| Repositorio: | Revista Ciencia y Tecnología |
| Idioma: | español |
| OAI Identifier: | oai:revistas.uteq.edu.ec:article/357 |
| Acceso en línea: | https://revistas.uteq.edu.ec/index.php/cyt/article/view/357 |
| Access Level: | acceso abierto |
| Palabra clave: | Ingeniería Social Phishing Machine Learning Deep Learning Ciber Seguridad Social Engineering Cybersecurity |
| Sumario: | In Computer Security, it does not matter whichSoftware or Hardware equipment is installed, becausealways the weakest link in this security chain, is the enduser. From this premise are used the different types ofSocial Engineering attacks, whose main objective isto obtain information almost directly from the users,with the purpose of using this information againstthemselves. There are several attack vectors of SocialEngineering, among which stand out: fake web pages,malign messages on social networks, and maliciousemails that ask for confidential information from usersor even redirect users to a fake web page (Phishing).The objective of this paper is to provide end users andother researchers with a look at the types of Phishingattacks that exist, and how they can be mitigated. Forthis, first, a systematic review of the literature in themain scientific sources is carried out, to characterizeand classify the different types of Phishing attacks, andsubsequently, the means by which these attacks can bemitigated are exposed and classified, ranging from auser awareness to the use of Machine Learning (ML)and Deep Learning (DL) techniques. |
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