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Fine Tuned Deep Neural Networks for Intrusion Detection System

Mr. D. P. Gaikwad, Mr. Amir Mukeri

Abstract


Intrusion detection system is used to gather and analyze data from networks for possible threats identification. It is used to record all significant events within network and takes corrective action when threats are detected. Some existing intrusion detection systems have experienced false positive or negative alarms. Recently, deep neural networks are being widely used to implement intrusion detection system to increase accuracy and reduce false alarms. Deep Neural networks are encouraged by the architecture depth of the human brain. These can possibly remove better portrayals from the information to make much better models. Tuning of hyper parameter of complex and computationally expensive deep networks is essential to advance classification accuracy by reducing training time. This hyper parameter tuning is also known as automated machine learning. In this paper, a deep neural network has been used for intrusion detection system. Different architecture of Multilayer perceptron has verified to learn best performance on intrusion detection system. Different hyper parameters of multilayer perceptron have tuned using different optimization techniques. Small to complex architecture of Perceptrons have used to carry out experiments. It was observed that smaller or simpler Neural Network is able to capture the function representation more closely than the more complex one. More complex deep networks with higher capacities are more prone to over fit. Hyper parameters tuning techniques such as dropout and regularization have helped to reduce complexities of model. It is also found that hyper parameter optimization techniques such as Bayesian and Hyperband optimization reduce considerable time and effort. These optimization techniques offer accuracies of 83% and 82% respectively on test dataset.


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References


Hongyu Liu, Bo Lang (2019), “Machine learning and deep learning methods for intrusion detection systems: A survey”, MDPI, Appl. Sci., Volume 9, Issue 20, pp. 4396, DOI:10.3390/app9204396.

W. Masduki, K. Ramli, F. A. Saputra, D. Sugiarto (10–13 August, 2015), “Study on implementation of machine learning methods combination for improving attacks detection accuracy on Intrusion Detection System (IDS)”, International Conference on Quality in Research (QiR), Lombok, Indonesia.

K. Patel, Kayur (October, 2010), “Lowering the barrier to applying machine learning”, Adjunct Proceedings of the 23nd Annual ACM Symposium on User Interface Software and Technology.

J. Bi, K. Zhang, X. Cheng (16–17 May, 2009), Intrusion detection based on RBF neural network”, International Symposium on Information Engineering and Electronic Commerce, Ternopil, Ukraine.

Matthias Feurer, Frank Hutter (2019), “Hyperparameter optimization”, The Springer Series on Challenges in Machine Learning, DOI: 10.1007/978-3-030-05318-5_1.

Bergstra, J., Bengio, Y. (2012), “Random search for hyper-parameter optimization”, J. of Mach. Learn. Res., Volume 13, Issue 10, pp. 281–305.

Klein, A., Falkner, S., Springenberg, J.T., Hutter, F. (2017), “Learning curve prediction with Bayesian neural networks”, Proceedings of the International Conference on Learning Representations.

Li, L., Jamieson, K., DeSalvo, G., Rostamizadeh, A., Talwalkar (2018), “Hyperband: A novel bandit-based approach to hyperparameter optimization”, J. of Mach. Learn. Res., Volume 18, pp. 1–52.

Chuanlong Yin et. al. (2017), “A deep learning approach for intrusion detection using recurrent neural networks”, IEEE Access, Volume 5, pp. 21954–21961, DOI: 10.1109/ACCESS.2017.2762418.

Jin Yang, Tao Li, Gang Liang, Wenbo He, Yue Zhao (2019), “A simple recurrent unit model-based intrusion detection system with DCGAN”, IEEE Access, Volume 7, pp. 83286–83296, DOI: 10.1109/ACCESS.2019.2922692.

Zhang, B., Yu, Y., Li, J. (20–24 May, 2018), “Network intrusion detection based on stacked sparse autoencoder and binary tree ensemble method”, In Proceedings of the 2018 IEEE International Conference on Communications Workshops (ICCWorkshops), Kansas City, MO, USA.

Ma, T., Wang, F., Cheng, J., Yu, Y., Chen, X. (2016), “A hybrid spectral clustering and deep neural network ensemble algorithm for intrusion detection in sensor networks”, Sensors, Volume 16, Issue 10, pp. 1701, DOI: 10.3390/s16101701.

Yuan, X., Li, C., Li, X. (29–31 May, 2017), “DeepDefense: Identifying DDoS attack via deep learning”, International Conference on Smart Computing (SMARTCOMP), Hong Kong, China.

Radford, B.J., Apolonio, L.M., Trias, A.J., Simpson, J.A. (2018), “Network traffic anomaly detection using recurrent neural networks”, Comp. and Soc.

Wang, W., Sheng, Y., Wang, J., Zeng, X., Ye, X., Huang, Y., Zhu, M. (2017), “HAST-IDS: Learning hierarchical spatial-temporal features using deep neural networks to improve intrusion detection”, IEEE Access, Volume 6, pp. 1792–1806, DOI: 10.1109/ACCESS.2017.2780250

Prechelt, L. (1998), “Early stopping-but when?” Neur. Net: Tricks of the Trade, Springer, Berlin, Heidelberg, pp. 55–69.

Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I. Salakhutdinov, R., (2014), “Dropout: a simple way to prevent neural networks from overfitting”, J. of Machine Learning. Volume 15, Issue 1, pp. 1929–1958.

Bergstra, J., Bengio, Y. (2012), “Random search for hyper-parameter optimization”, J. of Mach. Learn. Res., Volume 13, Issue 10, pp. 281–305.

Frazier, P. I. (2018), “A tutorial on Bayesian optimization”, Mach. Learn., pp. 1–22.




DOI: http://dx.doi.org/10.46610/JONSCN.2020.v06i02.002

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