Network Intrusion Detection with One Class Anomaly Detection Model based on Auto Encoder. |
Min, Byeoungjun
(Dept. of Computer Science, Sejong University)
Yoo, Jihoon (Dept. of Computer Science, Sejong University) Kim, Sangsoo (Agency for Defense Development) Shin, Dongil (Dept. of Computer Science, Sejong University) Shin, Dongkyoo (Dept. of Computer Science, Sejong University) |
1 | M. Thottan and C. Ji, "Anomaly detection in IP networks", IEEE Transactions on signal processing, vol. 51, no. 8, pp. 2191-2204, 2003. https://doi.org/10.1109/tsp.2003.814797 DOI |
2 | M. Ahmed, A. N. Mahmood and J. Hu, "A survey of network anomaly detection techniques", Journal of Network and Computer Applications, vol 60, pp. 19-31, 2016. https://doi.org/10.1016/j.jnca.2015.11.016 DOI |
3 | R. Longadge and S. Dongre, "Class imbalance problem in data mining review", 2013. Preprint at https://arxiv.org/abs/1305.1707 |
4 | S. Barua, M. M. Islam, X. Yao and K. Murase, "MWMOTE--majority weighted minority oversampling technique for imbalanced data set learning", IEEE Transactions on Knowledge and Data Engineering, vol. 26, no. 2, pp. 405-425, 2012. https://doi.org/10.1109/tkde.2012.232 DOI |
5 | L. M. Manevitz and M. Yousef, "One-class SVMs for document classification", Journal of machine Learning research, vol 2, pp. 139-154, 2001. https://dl.acm.org/doi/10.5555/944790.944808 |
6 | T. Luo and S. G. Nagarajan, "Distributed anomaly detection using autoencoder neural networks in wsn for iot", IEEE International Conference on Communications (ICC), pp. 1-6, 2018. https://doi.org/10.1109/icc.2018.8422402 DOI |
7 | C. Yin, Y. Zhu, J. Fei and X. He, "A deep learning approach for intrusion detection using recurrent neural networks", Ieee Access, vol. 5, pp. 21954-21961, 2017. https://doi.org/10.1109/access.2017.2762418 DOI |
8 | M. Tavallaee, E. Bagheri, W. Lu and A. A. Ghorbani, "A detailed analysis of the KDD CUP 99 data set", IEEE symposium on computational intelligence for security and defense applications, pp. 1-6, 2009. https://doi.org/10.1109/cisda.2009.5356528 DOI |
9 | J. Song, H. Takakura, Y. Okabe and Y. Kwon, "Correlation analysis between honeypot data and IDS alerts using one-class SVM", Intrusion Detection Systems, pp. 173-192, 2011. https://doi.org/10.5772/13951 |
10 | A. Borghesi, A. Bartolini, M. Lombardi, M. Milano and L. Benini, "Anomaly detection using autoencoders in high performance computing systems", In Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 9428-9433, 2019. https://doi.org/10.1609/aaai.v33i01.33019428 DOI |
11 | Y. Yang, K. Zheng, C. Wu and Y. Yang, "Improving the classification effectiveness of intrusion detection by using improved conditional variational autoencoder and deep neural network", Sensors, vol. 19, no. 11, pp. 2528, 2019. https://doi.org/10.3390/s19112528 DOI |
12 | A. Javaid, Q. Niyaz, W. Sun, and M. Alam, "A deep learning approach for network intrusion detection system", In Proceedings of the 9th EAI International Conference on Bio-inspired Information and Communications Technologies (formerly BIONETICS), pp. 21-26, 2016. https://dl.acm.org/doi/10.4108/eai.3-12-2015.2262516 DOI |
13 | D. S. Kim, H. N. Nguyen and J. S. Park, "Genetic algorithm to improve SVM based network intrusion detection system", In 19th International Conference on Advanced Information Networking and Applications (AINA papers), vol. 2, pp. 155-158, 2005. https://doi.org/10.1109/aina.2005.191 DOI |