1 |
S. H. Kang, I. S. Jeong, and H. S. Lim, "A feature set selection approach based on pearson correlation coefficient for real time attack detection," Convergence Security Journal, Vol.18, No.5_1, pp.59-66, 2018.
|
2 |
H. S. Chae, B. O. Jo, S. H. Choi, and T. K. Park, "Feature selection for intrusion detection using NSL-KDD," Recent Advances in Computer Science, pp.184-187, 2013.
|
3 |
N. F. Haq, A. R. Onik, and F. M. Shah, "An ensemble framework of anomaly detection using hybridized feature selection approach (HFSA)," In 2015 SAI Intelligent Systems Conference, pp.989-995, 2015.
|
4 |
R, Longadge and S, Dongre, "Class imbalance problem in data mining review," arXiv preprint arXiv:1305.1707, 2013.
|
5 |
T. H. Kim, S. H. Kang, "An Intrusion Detection System based on the Artificial Neural Network for Real Time Detection," Convergence Security Journal, Vol.17, No.1, pp.31-38, 2017.
|
6 |
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.
|
7 |
A. Tesfahun and D. L. Bhaskari, "Intrusion detection using random forests classifier with SMOTE and feature reduction," International Conference on Cloud & Ubiquitous Computing & Emerging Technologies, pp.127-132, 2013.
|
8 |
I. Guyon and A. Elisseeff, "An introduction to variable and feature selection," Journal of Machine Learning Research, Vol.3, pp.1157-1182, 2003.
|
9 |
H. He, Y. Bai, E. A. Garcia, and S. Li, "ADASYN: Adaptive synthetic sampling approach for imbalanced learning," IEEE international joint conference on neural networks, pp.1322-1328, 2008.
|
10 |
N. V. Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer, "SMOTE: synthetic minority over-sampling technique," Journal of Artificial Intelligence Research, Vol.16, pp.321-357, 2002.
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.
DOI
|
12 |
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.
|
13 |
H. Jiang, J. Nagra, and P. Ahammad, "Sok: Applying machine learning in security-a survey," arXiv preprint arXiv:1611.03186, 2016.
|
14 |
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.
DOI
|
15 |
K. He, X. Zhang, S. Ren, and J. Sun, "Delving deep into rectifiers: Surpassing human-level performance on imagenet classification," Proceedings of the IEEE International Conference on Computer Vision, pp.1026-1034, 2015.
|