RT info:eu-repo/semantics/conferenceProceedings
T1 Analysis of NetFlow Features’ Importance in Malicious Network Traffic Detection
A1 Campazas Vega, Adrián
A1 Crespo Martínez, Ignacio Samuel
A1 Guerrero Higueras, Ángel Manuel
A1 Álvarez Aparicio, Claudia
A1 Matellán Olivera, Vicente
A2 Arquitectura y Tecnologia de Computadores
K1 Cibernética
K1 Netflow features analysis
K1 K-Nearest Neighbors (KNN)
K1 Network traffic
K1 Machine learning
K1 Network security
K1 Malicious traffic detection
K1 1207.03 Cibernética
K1 1203.17 Informática
AB [EN] Malicious traffic detection allows for preventing cybersecurity-related threats. Machine learning algorithms are commonly used to detect such traffic in computer networks by analyzing packets. In wide-area networks, such as RedCAYLE (Red de Ciencia y Tecnolog´ıa de Castilla y Le´on), it is not possible to analyze every packet routed. So we pose that in such networks sampled flow data may be used to provide malicious traffic detection. This work presents the analysis carried out of the relevance that every NetFlow feature has in the K-Nearest Neighbors (KNN) algorithm in order to detect malicious traffic. Validation of the model has been carried out with real network data from RedCAYLE. Results show that it is necessary to train the models with sampled flow data. They also show that the nexthop feature has a negative influence on malicious traffic detection in wide-area networks such as RedCAYLE.
PB Springer
SN 9783030878719
LK https://hdl.handle.net/10612/21313
UL https://hdl.handle.net/10612/21313
NO Crespo-Martínez, I. S., Matellán, V., Guerrero-Higueras, Á. M., Campazas-Vega, A., & Álvarez-Aparicio, C. (2021). Analysis of NetFlow Features’ Importance in Malicious Network Traffic Detection. En 14th International Conference on Computational Intelligence in Security for Information Systems and 12th International Conference on European Transnational Educational (CISIS 2021 and ICEUTE 2021) (pp. 52-61). https://doi.org/10.1007/978-3-030-87872-6_6
DS BULERIA. Repositorio Institucional de la Universidad de León
RD Jul 12, 2024