Evaluating Features Selection on NSL-KDD Data-Set to Train a Support Vector Machine-Based Intrusion Detection System

The integrity of information and services is one of the more evident concerns in the world of global information security, due to the fact that it has economic repercussions on the digital industry. For this reason, big companies spend a lot of money on systems that protect them against cyber-attack...

Full description

Autores:

Tipo de recurso:

Fecha de publicación:

2019

Institución:

Universidad Tecnológica de Bolívar

Repositorio:

Repositorio Institucional UTB

Idioma:

eng

OAI Identifier:

oai:repositorio.utb.edu.co:20.500.12585/9137

Acceso en línea:

https://hdl.handle.net/20.500.12585/9137

Palabra clave:

Classification model
Data set
Dos Attacks
Feature selection
Machine learning
Support vector machine
Artificial intelligence
Classification (of information)
Denial-of-service attack
Intrusion detection
Learning systems
Network security
Statistical tests
Support vector machines
Classification models
Cyber-attacks
Data set
Features selection
Intrusion Detection Systems
Support vector machine models
Feature extraction

Rights

restrictedAccess

License

http://creativecommons.org/licenses/by-nc-nd/4.0/