1 |
C. Yin, L. Ma, L. Feng, Z. Yin and J. Wang, "A Feature Selection Algorithm towards Efficient Intrusion Detection," International Journal of Multimedia and Ubiquitous Engineering, vol.10, no.11, pp.253-264, 2015.
|
2 |
S. Y. Ohn, S. D. Chi, and M. Y. Han, "Feature Selection for Classification of Mass Spectrometric Proteomic Data Using Random Forest," The Korea Society For Simulation(KSS), Vol.22, No.4, pp.139-147, 2013.
DOI
|
3 |
W. Lee and S. Oh, "Efficient Feature Selection Based Near Real-Time Hybrid Intrusion Detection System," KIPS Tr. Comp. and Comm. Sys., vol.5, no.12, pp.471-480, Dec. 2016.
DOI
|
4 |
NSL-KDD Dataset [Internet], http://www.unb.ca/research/iscx/dataset/iscx-NSL-KDD-dataset.html.
|
5 |
M. Tavallaee, E. Bagheri, W. Lu, and A.-A. Ghorbani, "A detailed analysis of the kdd cup 99 data set," Computational Intelligence for Security and Defense Applications, CISDA 2009. IEEE Symposium on. IEEE, pp.1-6, 2009.
|
6 |
L. C. Molina, L. Belanche, and A. Nebot, "Feature selection algorithms: a survey and experimental evaluation," in Data Mining, ICDM 2003. Proceedings. 2002 IEEE International Conference on. IEEE. pp.306-313, 2002.
|
7 |
G. CHANDRASHEKAR, F. SAHIN, "A survey on feature selection methods," Computers & Electrical Engineering, Vol.40, No.1, pp.16-28, 2014.
DOI
|
8 |
L. Breiman, "Random forests," Machine learning, Vol.45, No.1, pp.5-32, 2001.
DOI
|
9 |
F. Baumann, A. Ehlers, K. Vogt, and B. Rosenhahn, "Cascaded Random Forest for Fast Object Detection," Scandinavian Conference on Image Analysis, Springer Berlin Heidelberg , pp. 131-142, 2013.
|
10 |
Y. Mishina, R. Murata, Y. Yamauchi, T. Yamashita, and H. Fujiyoshi, "Boosted random forest," IEICE TRANSACTIONS on Information and Systems, Vol.98, No.9, pp.1630-1636, 2015.
|
11 |
Kakavand, M., Mustapha, N., Mustapha, A., and Abdullah, M. T., "Effective Dimensionality Reduction of Payload-Based Anomaly Detection in TMAD Model for HTTP Payload," KSII Transactions on Internet and Information Systems, Vol. 10, No.8, pp.3884-3910, 2016
DOI
|
12 |
Ian H. Witten, Eibe Frank and Mark A. Hall, "Data Mining. 3rd," Trans. Lee. S. H, acorn, 2014.
|
13 |
M. A. Hall, "Correlation-based Feature Subset Selection for Machine Learning," doctoral dissertation, The University of Waikato, Canada, 1999.
|
14 |
H. Liu and R. Setiono, "A probabilistic approach to feature selection-A filter solution," in Proc. of 13th International Conference on Machine Learning, pp.319-327, 1996.
|
15 |
E. de la Hoz, A. Ortiz, J. Ortega, and E. de la Hoz, "Network anomaly classification by support vector classifiers ensemble and non-linear projection techniques," International Conference on Hybrid Artificial Intelligence Systems, Springer Berlin Heidelberg, vol.8073, pp.103-111, 2013.
|
16 |
Eid, H. F., Salama, M. A, Hassanien, A. E., and Kim, T. H, "Bi-layer behavioral-based feature selection approach for network intrusion classification," International Conference on Security Technology, Springer Berlin Heidelberg, vol. 259, pp.195-203, 2011.
|
17 |
S. Mukherjee and N. Sharma, "Intrusion detection using naive Bayes classifier with feature reduction," Procedia Technology, vol.4, pp.119-128, 2012.
DOI
|
18 |
H. F. Eid, A. E. Hassanien, T.-h. Kim, and S. Banerjee, "Linear correlation-based feature selection for network intrusion detection model," Advances in Security of Information and Communication Networks, Springer Berlin Heidelberg, vol.381, pp.240-248, 2013.
|
19 |
A. Frank and A. Asuncion, "UCI machine learning repository," 2010, http://archive.ics.uci.edu/ml
|
20 |
Abd-Eldayem and Mohamed M, "A proposed HTTP service based IDS," Egyptian Informatics Journal, vol.15, no.1, 13-24, 2014.
DOI
|