AI Classifiers Comparison for Network Anomaly Behavior Analysis

Producción científica: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

Resumen

As the number of devices connected through mobile networks increase exponentially, the communication demands a broader range of services that can be provided by the fifth generation of communication networks (5G). However, with the increase of devices sharing data and services, the surface of attack also increases, leading to known and unknown threats that can affect the quality of the communications. An efficient way to detect threats in this scenario, is by analyzing the behavior of the data in the network. The use of machine learning algorithms for this type of application is on the rise, as they are an efficient way for detecting and classifying anomalies. In this work, three different machine learning techniques are tested to detect cyberattacks targeting the integrity of the communication where Botnet, DoS and infiltration attacks were launched. Additionally, two dimensionality reduction techniques were compared to evaluate the performance of the AI techniques under constrained information scenarios. Results show that the selection of the machine learning technique is crucial to obtain better results for given attack scenarios and different dimensions.

Idioma originalInglés
Título de la publicación alojada2022 IEEE/ACS 19th International Conference on Computer Systems and Applications, AICCSA 2022 - Proceedings
EditorialIEEE Computer Society
ISBN (versión digital)9798350310085
DOI
EstadoPublicada - 2022
Evento19th IEEE/ACS International Conference on Computer Systems and Applications, AICCSA 2022 - Abu Dhabi, Emiratos Árabes Unidos
Duración: 5 dic. 20227 dic. 2022

Serie de la publicación

NombreProceedings of IEEE/ACS International Conference on Computer Systems and Applications, AICCSA
Volumen2022-December
ISSN (versión impresa)2161-5322
ISSN (versión digital)2161-5330

Conferencia

Conferencia19th IEEE/ACS International Conference on Computer Systems and Applications, AICCSA 2022
País/TerritorioEmiratos Árabes Unidos
CiudadAbu Dhabi
Período5/12/227/12/22

Nota bibliográfica

Publisher Copyright:
© 2022 IEEE.

Huella

Profundice en los temas de investigación de 'AI Classifiers Comparison for Network Anomaly Behavior Analysis'. En conjunto forman una huella única.

Citar esto