Abstract
Detecting unusual values from large volumes of information produced by network traffic has acquired considerable interest in the network security area. Having a system of detecting anomalous events in a time near their occurrence, it is important for all computer systems in a network. Detecting anomalous values can lead network administrators to identify system failures, take preventative actions and avoid a massive spread. Anomaly detection is a starting point to prevent attacks. In this article, we present a form of data pre-processing to identify anomalies using a supervised classification algorithm, image processing, parallel computing techniques and Graphical Processing Units.
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References
Barrionuevo, M., Lopresti, M., Miranda, N., Piccoli, M.: Un enfoque para la detección de anomalías en el tráfico de red usando imágenes y técnicas de computación de alto desempeño. In: XXII Congreso Argentino de Ciencias de la Computación, CACIC 2016, pp. 1166–1175 (2016)
Davis, J., Goadrich, M.: The relationship between precision-recall and ROC curves. In: ICML 2006: Proceedings of the 23rd International Conference on Machine Learning, New York, NY, USA, pp. 233–240. ACM (2006)
Gibson, D.: CompTIA Security+: Get Certified Get Ahead: SY0-201 Study Guide Createspace Independent Pub (2009). ISBN 9781439236369
Henao Ríos, J.L.: Definición De Un Modelo De Seguridad En Redes De Cómputo, Mediante El Uso De Técnicas De Inteligencia Artificial. Tesis presentada como requisito parcial para optar al título de Magíster en Ingeniería – Automatización Industrial, Universidad Nacional de Colombia (2012)
Lowe, D.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60, 91–110 (2004)
Miranda, N.: Cálculo en Tiempo Real de Identificadores Robustos para Objetos Multimedia Mediante una Arquitectura Paralela GPU-CPU, Tesis de Doctorado en Ciencias de la Computación, UNSL (2014)
Piccoli, M.F.: Computación de alto desempeño de GPU. 1era edic, La Plata Edulp (2011). ISBN 9789503407592
S. Institute: Transmission Control Protocol: DARPA Internet Program Protocol Specification. Defense Advanced Research Projects Agency, Information Processing Techniques Office (1981)
Tribak, H.: Análisis Estadístico de Distintas Técnicas de Inteligencia Artificial en Detección de Intrusos. Tesis Doctoral, Universidad de Granada (2012)
Wang, Y.: Statistical techniques for network security: modern statistically-based intrusion detection and protection. In: Network Traffic and Data, Information Science Reference - Imprint of: IGI Publishing (2008)
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Barrionuevo, M., Lopresti, M., Miranda, N., Piccoli, F. (2018). An Anomaly Detection Model in a LAN Using K-NN and High Performance Computing Techniques. In: De Giusti, A. (eds) Computer Science – CACIC 2017. CACIC 2017. Communications in Computer and Information Science, vol 790. Springer, Cham. https://doi.org/10.1007/978-3-319-75214-3_21
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DOI: https://doi.org/10.1007/978-3-319-75214-3_21
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