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http://dx.doi.org/10.7472/jksii.2020.21.2.121

A study on evaluation method of NIDS datasets in closed military network  

Park, Yong-bin (2nd R&D Institute, ADD)
Shin, Sung-uk (2nd R&D Institute, ADD)
Lee, In-sup (2nd R&D Institute, ADD)
Publication Information
Journal of Internet Computing and Services / v.21, no.2, 2020 , pp. 121-130 More about this Journal
Abstract
This paper suggests evaluating the military closed network data as an image which is generated by Generative Adversarial Network (GAN), applying an image evaluation method such as the InceptionV3 model-based Inception Score (IS) and Frechet Inception Distance (FID). We employed the famous image classification models instead of the InceptionV3, added layers to those models, and converted the network data to an image in diverse ways. Experimental results show that the Densenet121 model with one added Dense Layer achieves the best performance in data converted using the arctangent algorithm and 8 * 8 size of the image.
Keywords
dataset; Network Intrusion Detection System; Data evaluation; Machine Learning;
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1 Nour Moustafa and Jill Slay, "The evaluation of Network Anomaly Detection Systems: Statistical analysis of the UNSW-NB15 data set and the comparison with the KDD99 data sets" A Global Perspective, Vol. 25, Issue 1-3, pp. 18-31, 2016. https://doi.org/10.1080/19393555.2015.1125974
2 Mahbod Taballaee, Ebrahim Bagheri, Wei Lu and Ali A. Ghorbani, "A detailed Analysis of the KDD CUP 99 data set", 2009 IEEE Symposium on CISDA, pp.53-58. July 2019. https://doi.org/10.1109/CISDA.2009.5356528
3 Shane Barratt and Rishi Sharma, "A Note on the Inception Score", arXiv preprint arXiv:1801.01973, 2018. https://arxiv.org/abs/1801.01973
4 K. Shmelkov, C. Schmid and K. Alahari. "How good is my GAN?" 2018 ECCV pp. 213-229, 2018. http://openaccess.thecvf.com/content_ECCV_2018/html/Konstantin_Shmelkov_How_good_is_ECCV_2018_paper.html
5 Martin Arjovsky, Soumith Chintala, Leon Bottou, "Wasserstein GAN", Proceedings of the 34th Intenational Conference on Machine Learning, PMLR, 70:214-223, 2017. http://proceedings.mlr.press/v70/
6 Nataraj, L., Karthikeyanm, S., Jacob, G., Manjunath, B.S. "Malware images: visualization and automatic classification." Proceedings of the Conference on Visualizing for Cyber Securty, p. 4, 2011. https://doi.org/10.1145/2016904.2016908
7 Borjim A. "Pros and cons of GAN evaluation measures." Computer Vision and Image Understanding, 2019. https://doi.org/10.1016/j.cviu.2018.10.009
8 S. Revathi, Dr. A. Malathi, "A Detailed Analysis on NSL-KDD Dataset Using Various Machine Learning Techniques for Intrusion Detection", International Journal of Engineering Research & Technology (IJERT), Vol. 2 Issue 12, 2013. https://www.ijert.org/volume-02-issue-12-december-2013
9 S. Choi, S. Jang, Y. Kim, J.Kim, "Malware detection using malware image and deep learning", International Conference on Information and Communication Technology Convergence (ICTC), pp. 1193-1195, 2017. https://doi.org/10.1109/ICTC.2017.8190895