Secure Computer Network: Strategies and Challengers in Big Data Era

  • 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
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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References

Tulasi.B, R. S. Wagh, and B. S., “High performance computing and big data analytics - paradigms and challenges,” International Journal of Computer Applications, vol. 116, Abril 2015.

Y. Wang, Statistical Techniques for Network Security: Modern Statistically-Based Intrusion Detection and Protection. Hershey, PA: Information Science Reference - Imprint of: IGI Publishing, 2008.

W. E. Leland, W. Willinger, M. S. Taqqu, and D. V. Wilson, “On the self-similar nature of ethernet traffic,” vol. 25, no. 1, 1995.

D. Gibson, “Comptia security+: Get certified get ahead: Sy0-201 study guide createspace independent pub.,” 2009.

J. L. Henao R´ıos, Definici´on De Un Modelo De Seguridad En Redes De C´omputo, Mediante El Uso De T´ecnicas De Inteligencia Artificial. PhD thesis, Universidad Nacional de Colombia, 2012.

M. Schroeck, R. Shockley, S. Janet, D. Romero Morales, and P. Tufano, “Analytics: el uso de big data en el mundo real.,” in Escuela de Negocios Sa¨ıd en la Universidad de Oxford.

C. L. P. Chen and C. Zhang, “Data-intensive applications, challenges, techniques and technologies: A survey on big data,” Inf. Sci., vol. 275, pp. 314–347, 2014.

D. S. Terzi, R. Terzi, and S. Sagiroglu, “Big data Analytics for Network Anomaly Detection from Netflow Data,” IEEE, 2017.

A. Y. Nikravesh, S. A. Ajila, C. H. Lung, and W. Ding, “Mobile network traffic prediction using mlp, mlpwd, and svm,” pp. 402–409, June 2016.

T. Hind, An´alisis Estad´ıstico de Distintas T´ecnicas de Inteligencia Artificial en Detecci´on de Intrusos. PhD thesis, Universidad de Granada, 2012.

N. Miranda, C´alculo en Tiempo Real de Identificadores Robustos para Objetos Multimedia Mediante una Arquitectura Paralela GPU-CPU. PhD thesis, Universidad Nacional de San Luis, 2014.

M. F. Piccoli, Computaci´on de alto desempe˜no de GPU. Editorial de la Universidad Nacional de La Plata (EDULP), 2011.

A. M. Ghimes¸ and V. V. Patriciu, “Neural network models in big data analytics and cyber security,” in 2017 9th International Conference on Electronics, Computers and Artificial Intelligence (ECAI), pp. 1–6, June 2017.

S. Martig, S. Castro, M. Larrea, S. E. D. Urribarri, M. Escudero, and L. Ganuza, “Herramientas de visualización para la exploración de datos,” IX Workshop de Investigadores en Ciencias de la Computaci´on, 2007.

G. Hager and G. Wellein, Introduction to High Performance Computing for Scientists and Engineers. CRC Press, Inc., 1st ed., 2010.

Y. You, S. L. Song, H. Fu, A. Marquez, M. M. Dehnavi, K. J. Barker, K. W. Cameron, A. P. Randles, and G. Yang, “MIC-SVM: designing a highly efficient support vector machine for advanced modern multi-core and many-core architectures,” in 2014 IEEE 28th International Parallel and Distributed Processing Symposium, Phoenix, AZ, USA, May 19-23, 2014, pp. 809–818, 2014.

NVIDIA, “Nvidia cuda compute unified device architecture, c programming guide. version 7.5,” 2015.

D. C. Cires¸an, A. Giusti, L. M. Gambardella, and J. Schmidhuber, “Mitosis detection in breast cancer histology images with deep neural networks,” in Medical Image Computing and Computer-Assisted Intervention –MICCAI 2013 (K. Mori, I. Sakuma, Y. Sato, C. Barillot, and N. Navab, eds.), (Berlin, Heidelberg), pp. 411–418, Springer Berlin Heidelberg, 2013.

S. Chen, J. Qin, Y. Xie, J. Zhao, and P.-A. Heng, “A fast and flexible sorting algorithm with cuda,” in Algorithms and Architectures for Parallel Processing (A. Hua and S.-L. Chang, eds.), (Berlin, Heidelberg), pp. 281–290, Springer Berlin Heidelberg, 2009.

Y. Chen, Z. Qiao, S. Davis, H. Jiang, and K.-C. Li, “Pipelinedmulti-gpumapreduce for big-data processing,” in Computer and Information Science (R. Lee, ed.), (Heidelberg), pp. 231–246, Springer International Publishing, 2013.

S. Herrero-Lopez, “Accelerating svms by integrating gpus into mapreduce clusters,” in 2011 IEEE International Conference on Systems, Man, and Cybernetics, pp. 1298–1305, Oct 2011.

M. A. ud-din Khan, M. F. Uddin, and N. Gupta, “Seven v’s of big data understanding big data to extract value,” in Conference of the American Society for Engineering Education.

M. Barrionuevo, M. Lopresti, N. Miranda, and F. Piccoli, “Un enfoque para la detecci ´on de anomal´ıas en el tr´afico de red usando im´agenes y t´ecnicas de computaci ´on de alto desempe˜no,” XXII Congreso Argentino De Ciencias de la Computaci´on, pp. 1166–1175, 2016.

M. Barrionuevo, M. Lopresti, N. Miranda, and F. Piccoli, “An anomaly detection model in a lan using k-nn and high performance computing techniques,” Communications in Computer and Information Science, pp. 219–230, January 2018.

M. Barrionuevo, M. Lopresti, N. Miranda, and F. Piccoli, “P-sads: Un modelo de detecci ´on de anomal´ıas en una red lan,” 5to Congreso Nacional de Ingenier´ıa Inform´atica / Sistemas de Informaci´on Aspectos Legales y Profesionales y Seguridad Inform´atica, 2017.

D. G. Lowe, “Distinctive image features from scale-invariant keypoints,” International Journal of Computer Vision, vol. 60, pp. 91–110, Nov 2004.

Published
2018-12-12
How to Cite
Barrionuevo, M., Lopresti, M., Miranda, N., & Piccoli, F. (2018). Secure Computer Network: Strategies and Challengers in Big Data Era. Journal of Computer Science and Technology, 18(03), e28. https://doi.org/10.24215/16666038.18.e28
Section
Original Articles