A Convolutional Neural Network for Network Intrusion Detection System

Leila Mohammadpour, Teck Chaw Ling, Chee Sun Liew, Chun Yong Chong


System administrators can benefit from deploying Network Intrusion Detection Systems (NIDS) to find potential security breaches. However, security attacks tend to be unpredictable. There are many challenges to develop a flexible and effective NIDS in order to prevent high false alarm rates and low detection accuracy against unknown attacks. In this paper, we propose a deep learning method to implement an effective and flexible NIDS. We used a convolutional neural network (CNN), an advanced deep learning technique, on NSL-KDD, a benchmark dataset for network intrusion. Our experimental results of a 99.79% detection rate when compared against the NSL-KDD test dataset show that CNNs can be applied as a learning method for Intrusion Detection Systems (IDSs).

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A. Javaid, Q. Niyaz, W. Sun, and M. Alam, “A Deep Learning Approach for Network Intrusion Detection System,” in Proceedings of the 9th EAI International Conference on Bio-inspired Information and Communications Technologies (formerly BIONETICS), 2016, pp. 21–26.

S. Vieira, W. H. L. Pinaya, and A. Mechelli, “Using deep learning to investigate the neuroimaging correlates of psychiatric and neurological disorders: Methods and applications,” Neurosci. Biobehav. Rev., 2017.

G. LeCun, Yann and Bengio, Yoshua and Hinton, Y. LeCun, Y. Bengio, and G. E. Hinton, “Deep learning,” Nature, vol. 521, no. 7553, pp. 436–444, 2015.

Y. Yoo et al., “Deep Learning of Joint Myelin-T1w MRI Features on Normal-Appearing Brain Tissues Distinguishes Multiple Sclerosis from Healthy Controls,” Mult. Scler. J., vol. 23, no. 2, p. 315, 2017.

M. E. Aminanto and K. Kim, “Deep Learning in Intrusion Detection System: An Overview,” Proc. Int. Res. Conf. Eng. Technol., pp. 1–12, 2016.

D. Silver et al., “2016 - Mastering the game of Go with deep neural networks and tree search - DeepMind nature16961,” Nature, vol. 529, no. 7587, pp. 484–489, 2016.

E. Hodo, X. Bellekens, A. Hamilton, C. Tachtatzis, and R. Atkinson, “Shallow and Deep Networks Intrusion Detection System: A Taxonomy and Survey,” arXiv Prepr. arXiv1701.02145, pp. 1–43, 2017.

M. Tavallaee and E. B. and W. a. G. A. A. Lu, “‘A detailed analysis of the KDD CUP 99 data set,’ in Computational Intelligence for Security and Defense Applications,” in Computational Intelligence for Security and Defense Applications, 2009. CISDA 2009. IEEE Symposium on, 2009, pp. 1–6.

J. J. Davis and A. J. Clark, “Data preprocessing for anomaly based network intrusion detection: A review,” Comput. Secur., vol. 30, no. 6–7, pp. 353–375, 2011.

L. Mohammadpour, M. Hussain, A. Aryanfar, V. M. Raee, and F. Sattar, “Evaluating performance of intrusion detection system using support vector machines: Review,” Int. J. Secur. its Appl., vol. 9, no. 9, 2015.

D. Kwon, H. Kim, J. Kim, S. C. Suh, I. Kim, and K. J. Kim, “A survey of deep learning-based network anomaly detection,” Cluster Comput., pp. 1–13, 2017.

G. Litjens et al., “A Survey on Deep Learning in Medical Image Analysis,” CoRR, vol. 1702.05747, 2017.

R. C. O’Reilly, D. Wyatte, S. Herd, B. Mingus, and D. J. Jilk, “Recurrent processing during object recognition,” Front. Psychol., vol. 4, no. APR, pp. 1–38, 2013.

G. E. Hinton, N. Srivastava, A. Krizhevsky, I. Sutskever, and R. R. Salakhutdinov, “Improving neural networks by preventing co-adaptation of feature detectors,” arXiv Prepr. arXiv1207.0580, 2012.

M. D. Zeiler and R. Fergus, “Visualizing and Understanding Convolutional Networks arXiv:1311.2901v3 [cs.CV] 28 Nov 2013,” in Computer Vision–ECCV 2014, 2014, vol. 8689, pp. 818–833.

Y. Shi et al., “Early endothelial progenitor cells as a source of myeloid cells to improve the pre-vascularisation of bone constructs,” Eur. Cells Mater., vol. 27, pp. 64–80, 2014.

R. Singh, H. Kumar, and R. K. Singla, “An intrusion detection system using network traffic profiling and online sequential extreme learning machine,” Expert Syst. Appl., vol. 42, no. 22, pp. 8609–8624, 2015.

H. S. and B. S. D., “KDD Cup ’99 Dataset,” 1999. [Online]. Available: http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html.


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