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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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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