Secure Computer Network: Strategies and Challengers in Big Data Era

• Main Authors: Mercedes Barrionuevo, Mariela Lopresti, Natalia Miranda, Fabiana Piccoli
• Format: Article
• Language: English
• Published: Postgraduate Office, School of Computer Science, Universidad Nacional de La Plata 2018-12-01
• Series: Journal of Computer Science and Technology
• Subjects: anomalies and attacks; big data; computer network; high performance computing; machine learning; network security
• Online Access: http://journal.info.unlp.edu.ar/JCST/article/view/1116
• ISSN: 1666-6046; 1666-6038

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.