Network anomaly classification by support vector classifiers ensemble and non-linear projection techniques
Clasificación de anomalías de red por conjuntos de clasificadores de vectores de soporte y técnicas de proyección no lineales
De La Hoz, Eduardo
De la Hoz, Emiro
Show full item record
Show full item record
AbstractNetwork anomaly detection is currently a challenge due to the number of different attacks and the number of potential attackers. Intrusion detection systems aim to detect misuses or network anomalies in order to block ports or connections, whereas firewalls act according to a predefined set of rules. However, detecting the specific anomaly provides valuable information about the attacker that may be used to further protect the system, or to react accordingly. This way, detecting network intrusions is a current challenge due to growth of the Internet and the number of potential intruders. In this paper we present an intrusion detection technique using an ensemble of support vector classifiers and dimensionality reduction techniques to generate a set of discriminant features. The results obtained using the NSL-KDD dataset outperforms previously obtained classification rates
The following license files are associated with this item:
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-ShareAlike 4.0 International
Showing items related by title, author, creator and subject.
Feature selection, learning metrics and dimension reduction in training and classification processes in intrusion detection systems Mendoza Palechor, Fabio; De la Hoz Manotas, Alexis Kevin; De la Hoz, Emiro; Ariza Colpas, Paola Patricia (Journal of Theoretical and Applied Information Technology, 2015-12-20)This research presents an IDS prototype in Matlab that assess network traffic connections contained in the NSL-KDD dataset, comparing feature selection techniques available in FEAST toolbox, refining prior results applying ...
Design and development of a custom system of technology surveillance and competitive intelligence in SMEs Silva, Jesus; Vidal Pacheco, Lucelys del Carmen; Parra Negrete, Kevin; Combita Niño, Johana Patricia; Pineda Lezama, Omar Bonerge; Izquierdo Varela, Noel (Procedia Computer Science, 2019)Making strategic decisions is a complex process that requires reliable and up-to-date information. It is therefore necessary to have tools that facilitate the information management. Technology Surveillance (TS) and ...
Silva, Jesus; MOJICA HERAZO, JULIO CESAR; Rojas Millán, Rafael Humberto; Pineda Lezama, Omar Bonerge; Morgado Gamero, W.B.; Varela Izquierdo, Noel (Procedia Computer Science, 2019)Applications based on Artificial Neural Networks (ANN) have been developed thanks to the advance of the technological progress which has permitted the development of sales forecasting on consumer products, improving the ...