Memorias de investigación
Communications at congresses:
Anomaly Detection Using Gaussian Mixture Probability Model to Implement Intrusion Detection System
Year:2019

Research Areas
  • Electronic technology and of the communications

Information
Abstract
Network intrusion detection systems (NIDS) detect attacks or anomalous network traffic patterns in order to avoid cybersecurity issues. Anomaly detection algorithms are used to identify unusual behavior or outliers in the network traffic in order to generate alarms. Traditionally, Gaussian Mixture Models (GMMs) have been used for probabilistic-based anomaly detection NIDS. We propose to use multiple simple GMMs to model each individual feature, and an asymmetric voting scheme that aggregates the individual anomaly detectors to provide. We test our approach using the NSL dataset. We construct the normal behavior models using only the samples labelled as normal in this dataset and evaluate our proposal using the official NSL testing set. As a result, we obtain a F1-score over 0.9, outperforming other supervised and unsupervised proposals.
International
Si
Congress
International Conference on Hybrid Artificial Intelligence Systems
960
Place
León
Reviewers
Si
ISBN/ISSN
978-3-030-29859-3
https://doi.org/10.1007/978-3-030-29859-3_55
Start Date
04/09/2019
End Date
06/09/2019
From page
648
To page
659
Hybrid Artificial Intelligent Systems
Participants

Research Group, Departaments and Institutes related
  • Creador: Grupo de Investigación: Laboratorio de Sistemas Integrados (LSI)
  • Centro o Instituto I+D+i: Centro de Investigación en Simulación Computacional
  • Departamento: Ingeniería Electrónica