1 |
M. M. a. M. V. Valter Vasic, "Lightweight and adaptable solution for security agility," KSII Transactions on Internet and Information Systems, vol. 10, pp. 1212-1228, March, 2016.
|
2 |
M. Jo, L. Han, D. Kim, and H. P. In, "Selfish attacks and detection in cognitive radio Ad-Hoc networks," IEEE Network, vol. 27, pp. 46-50, June, 2013.
DOI
|
3 |
Z. Qi, Y. Tian, and Y. Shi, "Robust twin support vector machine for pattern classification," Pattern Recognition, vol. 46, pp. 305-316, January, 2013.
DOI
|
4 |
S. Maji, A. C. Berg, and J. Malik, "Efficient Classification for Additive Kernel SVMs," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, pp. 66-77, January, 2013.
DOI
|
5 |
Y. Zhang, J. Duchi, and M. Wainwright, "Divide and conquer kernel ridge regression: a distributed algorithm with minimax optimal rates," Journal of Machine Learning Research, vol. 16, pp. 3299-3340, December, 2015.
|
6 |
S. F. Jianjun Li, Zhihui Wang, Haojie Li and Chin-Chen Chang, "An Optimized CLBP Descriptor Based on a Scalable Block Size for Texture Classification," KSII Transactions on Internet and Information Systems, vol. 11, pp. 288-301, January, 2017.
|
7 |
X. Zhang and M. H. Mahoor, "Task-dependent multi-task multiple kernel learning for facial action unit detection," Pattern Recognition, vol. 51, pp. 187-196, March, 2016.
DOI
|
8 |
S. Hare, S. Golodetz, A. Saffari, V. Vineet, M. M. Cheng, S. L. Hicks, et al., "Struck: Structured Output Tracking with Kernels," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 38, pp. 2096-2109, December, 2015.
|
9 |
H. Xue, S. Chen, and Q. Yang, "Structural Regularized Support Vector Machine: A Framework for Structural Large Margin Classifier," IEEE Transactions on Neural Networks, vol. 22, pp. 573-587, April, 2011.
DOI
|
10 |
Myerson RB. Game Theory. Harvard University Press Books, 2013.
|
11 |
C. Modi, D. Patel, B. Borisaniya, H. Patel, A. Patel, and M. Rajarajan, "A survey of intrusion detection techniques in Cloud," Journal of Network and Computer Applications, vol. 36, pp. 42-57, January, 2013.
DOI
|
12 |
S. Mukkamala, G. Janoski, and A. Sung, "Intrusion detection using neural networks and support vector machines," in Proc. of Neural Networks, 2002. IJCNN '02. Proceedings of the 2002 International Joint Conference on, pp. 1702-1707, May 12-17, 2002.
|
13 |
M. Ektefa, S. Memar, F. Sidi, and L. S. Affendey, "Intrusion detection using data mining techniques," in Proc. of 2010 International Conference on Information Retrieval & Knowledge Management (CAMP), pp. 200-203, March 17-18, 2010.
|
14 |
W. Hu, Y. Liao, and V. R. Vemuri, "Robust Support Vector Machines for Anomaly Detection in Computer Security," in Proc. of International Conference on Machine Learning and Applications - Icmla 2003, pp. 168-174, June 23-24, 2003.
|
15 |
S.-J. Horng, M.-Y. Su, Y.-H. Chen, T.-W. Kao, R.-J. Chen, J.-L. Lai, et al., "A novel intrusion detection system based on hierarchical clustering and support vector machines," Expert Systems with Applications, vol. 38, pp. 306-313, January, 2011.
DOI
|
16 |
H. LI, X.-H. GUAN, X. ZAN, and C.-Z. HAN, "Network intrusion detection based on support vector machine," Journal of Computer Research and Development, vol. 6, pp. 799-807, June, 2003.
|
17 |
K. L. Li, H. K. Huang, S. F. Tian, Z. P. Liu, and Z. Q. Liu, "Fuzzy multi-class support vector machine and application in intrusion detection," Chinese Journal of Computers, vol. 28, pp. 274-280, February, 2005.
|
18 |
H. Chih-Wei and L. Chih-Jen, "A comparison of methods for multiclass support vector machines," IEEE Transactions on Neural Networks, vol. 13, pp. 415-425, August, 2002.
DOI
|
19 |
C.-C. Chang and C.-J. Lin, "LIBSVM: A library for support vector machines," ACM Trans. Intell. Syst. Technol., vol. 2, pp. 1-27, April, 2011.
|
20 |
P. J. Reny, "Nash equilibrium in discontinuous games," Economic Theory, vol. 61, pp. 553-569, March, 2016.
DOI
|
21 |
J. Chorowski, J. Wang, and J. M. Zurada, "Review and performance comparison of SVM- and ELM-based classifiers," Neurocomputing, vol. 128, pp. 507-516, March, 2014.
DOI
|
22 |
J. M. Fossaceca, T. A. Mazzuchi, and S. Sarkani, "MARK-ELM: Application of a novel Multiple Kernel Learning framework for improving the robustness of Network Intrusion Detection," Expert Systems with Applications, vol. 42, pp. 4062-4080, May, 2015.
DOI
|
23 |
R. P. Lippmann and R. K. Cunningham, "Guide to Creating Stealthy Attacks for the 1999 DARPA Off-Line Intrusion Detection Evaluation," Computer Networks, vol. 34, pp. 579-595, January, 1999.
|
24 |
W. Hu, J. Gao, Y. Wang, O. Wu, and S. Maybank, "Online Adaboost-Based Parameterized Methods for Dynamic Distributed Network Intrusion Detection," IEEE Transactions on Cybernetics, vol. 44, pp. 66-82, January, 2014.
DOI
|
25 |
J. Kevric, S. Jukic, and A. Subasi, "An effective combining classifier approach using tree algorithms for network intrusion detection," Neural Computing and Applications, pp. 1-8, June, 2016.
|
26 |
D. R. Wilson and T. R. Martinez, "Improved heterogeneous distance functions," Journal of Artificial Intelligence Research, vol. 6, pp. 1-34, June, 2000.
|
27 |
S. Rastegari, P. Hingston, and C.-P. Lam, "Evolving statistical rulesets for network intrusion detection," Applied Soft Computing, vol. 33, pp. 348-359, August, 2015.
DOI
|
28 |
M.-H. Chen, P.-C. Chang, and J.-L. Wu, "A population-based incremental learning approach with artificial immune system for network intrusion detection," Engineering Applications of Artificial Intelligence, vol. 51, pp.171-181, May, 2016.
DOI
|
29 |
J. Wei, R. Zhang, J. Liu, X. Niu, and Y. Yang, "Defense Strategy of Network Security based on Dynamic Classification," Ksii Transactions on Internet & Information Systems, vol. 9, pp. 5116-5134, December, 2015.
|
30 |
C.-F. Tsai, Y.-F. Hsu, C.-Y. Lin, and W.-Y. Lin, "Intrusion detection by machine learning: A review," Expert Systems with Applications, vol. 36, pp. 11994-12000, December, 2009.
DOI
|