Abstract
With the growth of network activities and data sharing, there is also increased risk of threats and malicious attacks. Intrusion detection refers to the act of successfully identifying and thwarting malicious attacks. Traditionally, the help of network security experts is sought owing to their familiarity with the network technologies and broad knowledge. Recently, data mining techniques have been increasingly adopted to perform network intrusion detection. This paper presents the comparison between multi-layer perceptron and radial basis function networks for designing network intrusion detection system. Multi-layer perceptron proved to be more effective than radial basis function when applied on the benchmark NSL_KDD dataset.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
McHugh, John. “Testing intrusion detection systems: a critique of the 1998 and 1999 darpa intrusion detection system evaluations as performed by lincoln laboratory.” ACM Transactions on Information and System Security (TISSEC) 3.4, 2000, pp. 262–294.
Surana, S. “Intrusion Detection using Fuzzy Clustering and Artificial Neural Network”. Advances in Neural Networks, Fuzzy Systems and Artificial Intelligence, ISBN-978-960-474-379-7, 2013, pp. 209–217.
Dokas, P., Ertoz, L., Kumar, V., Lazarevic, A., Srivastava, J., & Tan, P. N. “Data mining for network intrusion detection. In Proc. NSF Workshop on Next Generation Data Mining”, 2002, pp. 21–30.
Mulay, S. A., Devale, P. R., & Garje, G. V. “Intrusion detection system using support vector machine and decision tree. International Journal of Computer Applications”, 3(3), 2010, pp. 40–43.
Panda, M., & Patra, M. R. (2007). Network intrusion detection using naive bayes. International journal of computer science and network security, 7(12), 258–263.
Rodda, Sireesha, Erothi, Uma Shankar Rao. “Class Imbalance Problem in the Network Intrusion Detection Systems”. International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT), 2016, pp. 2685–2688.
Acknowledgements
The author expresses a deep sense of gratitude to Science and Engineering Research Board (SERB), Ministry of Science and Technology, Government of India, Grant Number SB/FTP/ETA-0180/2014, for providing financial support to this work.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Rodda, S. (2018). Network Intrusion Detection Systems Using Neural Networks. In: Bhateja, V., Nguyen, B., Nguyen, N., Satapathy, S., Le, DN. (eds) Information Systems Design and Intelligent Applications. Advances in Intelligent Systems and Computing, vol 672. Springer, Singapore. https://doi.org/10.1007/978-981-10-7512-4_89
Download citation
DOI: https://doi.org/10.1007/978-981-10-7512-4_89
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-7511-7
Online ISBN: 978-981-10-7512-4
eBook Packages: EngineeringEngineering (R0)