A comparison of the NSL-KDD dataset and its predecessor the KDD Cup ’99 dataset
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This study examines three datasets, notably the KDD Cup '99 and the NSL-KDD datasets, which are commonly used in intrusion detection research in computer networks. The KDD Cup '99 dataset contains five million records, each with 41 attributes that may be used to categorize malicious assaults into four categories: Probe, DoS, U2R, and R2L. Because it was developed by simulation over a virtual computer network, the KDD Cup '99 dataset cannot reflect real traffic statistics. Duplicate and redundant records from the KDD Cup '99 dataset are eliminated from the training and test sets, respectively, in the NSL-KDD dataset.
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Protić, D. 2016. Neural Cryptography. Vojnotehnički glasnik/Military Technical Courier, 64(2), pp.483-495. Available at: https://doi.org/10.5937/vojtehg64-8877.
SIGKDD - KDD Cup. KDD Cup 1999: Computer network intrusion detection.
Available at: https://www.kdd.org.
Aggarwal, P. & Sharma, S.K. 2015. Analysis of KDD Dataset Attributes – Class Wise for Intrusions Detection. In: Procedia Computer Science, 57, pp.842- 851. . Available at: https://doi.org/10.1016/j.procs.2015.07.490.
Tavallaee, M., Bagheri, E., Lu, W. & Ghorbani Ali, A. 2009. A Detailed Analysis of the KDD CUP ‘99 Data Set. In: Proceedings of the 2009 IEEE Symposium on Computational Intelligence in Security and Defense Applications. Ottawa, ON, Canada. Available at: https://doi.org/10.1109/CISDA.2009.5356528.
Gifty Jeya, P., Ravichandran, M. & Ravichandran, C.S. 2012. Efficient Classifier for R2L and U2R Attacks. International Journal of Computer Applications, 45(21), pp.28-32.
Available at: http://www.ijcaonline.org/archives/volume45/number21/7076-9751.
Al-Dhafian, B., Ahmad, I. & Al-Ghamid, A. 2015. An Overview of the Current Classification Techniques. In: International Conference on Security and Management, Las Vegas, USA, pp.82-88.
Paliwal, S. & Gupta, R. 2012. Denial-of-Service, Probing & Remote to User (R2L) Attack Detection using Genetic Algorithm. International Journal of Computer Applications, 60(19), pp.57-62. Available at: http://www.ijcaonline.org/archives/volume60/number19/9813-4306.
Kolez, A., Chowdhury, A. & Alspector, J. 2003.Workshop on Learning from Imbalanced Data Sets (II), Whashington.
Maček, N. & Milosavljević, M. 2013. Critical Analysis of the KDD Cup ’99 data set and research methodology for machine learning. In: Proceedings of the 57th ETRAN conference, Zlatibor, pp.(VI 2.3.1-4.).
Bukola, O. & Adetunmbi, A.O. 2016. Auto-Immunity Dendritic Cell Algorithm. In: International Journal of Computer Applications, 137(2), pp.10-17. Available at: https://doi.org/10.5120/ijca2016908689.
Revathi, S. & Malathi, A. 2013. A Detailed Analysis on NSL-KDD Dataset Using Various Machine Learning Techniques for Intrusion Detection. International Journal of Engineering Research & Technology, 2(12), pp.1848-1853.
Kavitha, P. & Usha, M. 2014. Anomaly based intrusion detection in WLAN using discrimination algorithm combined with Naïve Bayesian classifier. Journal of Theoretical and Applied Information Technology, 62(1), pp.77-84. Available at: http://www.jatit.org/volumes/Vol62No1/11Vol62No1.pdf.
Nkiama, H., Said, S.Z.M. & Saidu, M. 2016. A Subset Feature Elimination Mechanisms for Intrusion Detection System. International Journal of Advanced Computer Science and Application, 7(4), pp.148-157.Available at: https://doi.org/10.14569/IJACSA.2016.070419
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