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2018 | OriginalPaper | Chapter

A Study on Comparison of KDD CUP 99 and NSL-KDD Using Artificial Neural Network

Authors : Hyunjung Ji, Donghwa Kim, Dongkyoo Shin, Dongil Shin

Published in: Advances in Computer Science and Ubiquitous Computing

Publisher: Springer Singapore

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Abstract

Computer network face many security problems because various smart devices using computer networks are being developed and are rapidly spreading. Therefore, an Intrusion Detection System (IDS) is necessary for network security. There are two typical datasets for IDS: KDD CUP 99 (KDD’99) and NSL-KDD. In this paper, we introduced KDD’99 and NSL-KDD and analysis these datasets using an artificial neural network. KDD’99 shows a higher score for overall accuracy, but falls behind in its classification accuracy per category.

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previous chapter System Information Comparison and Analysis Technology for Cyber Attacks
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Literature
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Debar, H., Dacier, M., Wespi, A.: Towards a taxonomy of intrusion-detection systems. Comput. Netw. 31(8), 805–822 (1999)CrossRef
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Özgür, Erdem, H.: A review of KDD99 dataset usage in intrusion detection and machine learning between 2010 and 2015. PeerJ Preprints 4, e1954v1 (2016)
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Ibrahim, L.M., Basheer, D.T., Mahmod, M.S.: A comparison study for intrusion database (KDD99, NSL-KDD) based on self organization map (SOM) artificial neural network. J. Eng. Sci. Technol. 8(1), 107–119 (2013)
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Tavallaee, M., Bagheri, E., Lu, W., Ghorbani, A.A.: A detailed analysis of the KDD CUP 99 data set. In: IEEE Symposium on IEEE Computational Intelligence in Security and Defense Applications, CISDA 2009, pp. 1–6 (2009)
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Ingre, B., Yadav, A.: Performance analysis of NSL-KDD dataset using ANN. In: 2015 International Conference on IEEE Signal Processing and Communication Engineering Systems (SPACES), pp. 92–96 (2015)
Metadata
Title
A Study on Comparison of KDD CUP 99 and NSL-KDD Using Artificial Neural Network
Authors
Hyunjung Ji
Donghwa Kim
Dongkyoo Shin
Dongil Shin
Copyright Year
2018
Publisher
Springer Singapore
Book
Advances in Computer Science and Ubiquitous Computing

Print ISBN: 978-981-10-7604-6

Electronic ISBN: 978-981-10-7605-3

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-981-10-7605-3_74