Secure Computer Network: Strategies and Challengers in Big Data Era

Authors

  • Mercedes Barrionuevo LIDIC, Universidad Nacional de San Luis, San Luis, Argentina
  • Mariela Lopresti LIDIC, Universidad Nacional de San Luis, San Luis, Argentina
  • Natalia Miranda LIDIC, Universidad Nacional de San Luis, San Luis, Argentina
  • Fabiana Piccoli LIDIC, Universidad Nacional de San Luis, San Luis, Argentina

DOI:

https://doi.org/10.24215/16666038.18.e28

Keywords:

Anomalies and Attacks, Big Data, Computer Network, High Performance Computing, Machine Learning, Network Security

Abstract

As computer networks have transformed in essential tools, their security has become a crucial problem for computer systems. Detecting unusual values from
large volumes of information produced by network traffic has acquired huge interest in the network security area. Anomaly detection is a starting point to
prevent attacks, therefore it is important for all computer systems in a network have a system of detecting anomalous events in a time near their occurrence. Detecting these events can lead network administrators to identify system failures, take preventive actions and avoid a massive damage.
This work presents, first, how identify network traffic anomalies through applying parallel computing techniques and Graphical Processing Units in two algorithms, one of them a supervised classification algorithm and the other based in traffic image processing.
Finally, it is proposed as a challenge to resolve the anomalies detection using an unsupervised algorithm as Deep Learning.

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Published

2018-12-12

How to Cite

[1]
“Secure Computer Network: Strategies and Challengers in Big Data Era”, JCS&T, vol. 18, no. 03, p. e28, Dec. 2018, doi: 10.24215/16666038.18.e28.

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