Abstract
Intrusion Detection is crucial in cybersecurity. So is the ability to identify the myriad of attacks. Artificial Neural Networks are an established and proven method of accurate classification. There are approaches to make ANN models faster by applying Principal Component Analysis as a feature extractor. However, ANNs are extremely versatile, a wide range of setups can achieve significantly different classification results. The main contribution of this paper is the evaluation of the way the hyperparameters can influence the final classification result. In this paper, a wide range of ANN setups is put to comparison, and the finest arrangement achieves the multi-class classification accuracy of 99.909% on an established benchmark dataset.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
McGraw, G., Morrisett, G.: Attacking malicious code: a report to the Infosec Research Council. IEEE Softw. 17(5), 33–41 (2000)
Bielec, A.: Analysis of a polish bankbot (2018)
Kelion, L.: eBay redirect attack puts buyers’ credentials at risk (2014)
Mutton, P.: Hackers still exploiting eBay’s stored XSS vulnerabilities in 2017 (2017)
Lee, D.: MyFitnessPal breach affects millions of under armour users (2018)
Idika, N., Mathur, A.: A survey of malware detection techniques. Purdue University, March 2007
Canfora, G., Di Sorbo, A., Mercaldo, F., Visaggio, C.A.: Obfuscation techniques against signature-based detection: a case study. In: 2015 Mobile Systems Technologies Workshop (MST), pp. 21–26, May 2015
Sani, Y., Mohamedou, A., Ali, K., Farjamfar, A., Azman, M., Shamsuddin, S.: An overview of neural networks use in anomaly intrusion detection systems. In: 2009 IEEE Student Conference on Research and Development (SCOReD), pp. 89–92, November 2009
Haddadi, F., Khanchi, S., Shetabi, M., Derhami, V.: Intrusion detection and attack classification using feed-forward neural network. In: 2010 Second International Conference on Computer and Network Technology, pp. 262–266, April 2010
Gong, W., Fu, W., Cai, L.: A neural network based intrusion detection data fusion model. In: 2010 Third International Joint Conference on Computational Science and Optimization, vol. 2, pp. 410–414, May 2010
Mukhopadhyay, I., Chakraborty, M., Chakrabarti, S., Chatterjee, T.: Back propagation neural network approach to intrusion detection system. In: 2011 International Conference on Recent Trends in Information Systems, pp. 303–308, December 2011
Sonawane, H.A., Pattewar, T.A.: A comparative performance evaluation of intrusion detection based on neural network and PCA. In: 2015 International Conference on Communications and Signal Processing (ICCSP), pp. 0841–0845, April 2015
Pattewar, T.M., Sonawane, H.A.: Neural network based intrusion detection using Bayesian with PCA and KPCA feature extraction. In: 2015 IEEE International Conference on Computer Graphics, Vision and Information Security (CGVIS), pp. 83–88, November 2015
Van, N.T.T., Thinh, T.N.: Accelerating anomaly-based IDS using neural network on GPU. In: 2015 International Conference on Advanced Computing and Applications (ACOMP), pp. 67–74, November 2015
Subba, B., Biswas, S., Karmakar, S.: A neural network based system for intrusion detection and attack classification. In: 2016 Twenty Second National Conference on Communication (NCC), pp. 1–6, March 2016
Potluri, S., Diedrich, C.: Accelerated deep neural networks for enhanced intrusion detection system. In: 2016 IEEE 21st International Conference on Emerging Technologies and Factory Automation (ETFA), pp. 1–8, September 2016
Maimon, O., Rokach, L.: Data Mining and Knowledge Discovery Handbook, 2nd edn. Springer, Boston (2010). https://doi.org/10.1007/978-0-387-09823-4
Nunes, I., et al.: Artificial Neural Networks A Practical Course. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-43162-8
Bassis, S., Esposito, A., Morabito, F.C., Pasero, E.: Advances in Neural Networks. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-18164-6
Charu, C.A.: Neural Networks and Deep Learning a Textbook (2018)
Abadi, M., et al.: TensorFlow: large-scale machine learning on heterogeneous systems (2015). Software: tensorflow.org
Chollet, F., et al.: Keras (2015). https://github.com/fchollet/keras
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Pawlicki, M., Kozik, R., Choraś, M. (2019). Artificial Neural Network Hyperparameter Optimisation for Network Intrusion Detection. In: Huang, DS., Bevilacqua, V., Premaratne, P. (eds) Intelligent Computing Theories and Application. ICIC 2019. Lecture Notes in Computer Science(), vol 11643. Springer, Cham. https://doi.org/10.1007/978-3-030-26763-6_72
Download citation
DOI: https://doi.org/10.1007/978-3-030-26763-6_72
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-26762-9
Online ISBN: 978-3-030-26763-6
eBook Packages: Computer ScienceComputer Science (R0)