Machine learning y seguridad: detección de correos falsos y detección de intrusos
In recent years, the number of organizations and individuals using technology to support daily tasks has grown consistently. As technology is used by a greater number of people, the community also faces more information security issues; new threats emerge every day, which pose risks to the informati...
- Autores:
-
Sotelo Londoño, Nicolás
León Alzáte, Mateo
- Tipo de recurso:
- Trabajo de grado de pregrado
- Fecha de publicación:
- 2020
- Institución:
- Universidad de los Andes
- Repositorio:
- Séneca: repositorio Uniandes
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.uniandes.edu.co:1992/51480
- Acceso en línea:
- http://hdl.handle.net/1992/51480
- Palabra clave:
- Detección de anomalías (Seguridad en computadores)
Sistemas de información en administración
Sistemas de almacenamiento y recuperación de información
Protección de datos
Seguridad en computadores
Tecnología de la información
Ingeniería
- Rights
- openAccess
- License
- http://creativecommons.org/licenses/by-nc-nd/4.0/
| Summary: | In recent years, the number of organizations and individuals using technology to support daily tasks has grown consistently. As technology is used by a greater number of people, the community also faces more information security issues; new threats emerge every day, which pose risks to the information of all users. One of the most common security problems is the presence of malicious programs (malware) and nowadays, Machine Learning (ML) is one of the techniques currently being explored to detect malware. In this context, this project explores the advantages and disadvantages of Machine Learning (ML) in the detection of patterns and anomalies in two environments of great relevance in the security world: phishing and intrusion detection. For organizations it is important to detect these two situations, as it allows preventing different types of attacks. |
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