Secure Computer Network: Strategies and Challengers in Big Data Era

• Autores: Barrionuevo, Mercedes; Lopresti, Mariela; Miranda, Natalia Carolina; Piccoli, Fabiana
• Año de publicación: 2018
• Idioma: inglés
• Tipo de recurso: documento de conferencia
• Materia: Ciencias Informáticas; computer network, network security, anomalies and attacks, big data, high performance computing, machine learning; Algorithms
• Institución: Universidad Nacional de La Plata

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.
Facultad de Informática