IEEE/CAA Journal of Automatica Sinica
Citation: | Shaohua Teng, Naiqi Wu, Haibin Zhu, Luyao Teng and Wei Zhang, "SVM-DT-Based Adaptive and Collaborative Intrusion Detection," IEEE/CAA J. Autom. Sinica, vol. 5, no. 1, pp. 108-118, Jan. 2018. doi: 10.1109/JAS.2017.7510730 |
[1] |
S. H. Teng, N. Q. Wu, W. Zhang, and X. F. Fu, "Cooperative intrusion detection based on object monitoring, " Acta Sci. Nat. Univ. Suny., vol. 47, no. 6, pp. 76-81, Nov. 2008. http://en.cnki.com.cn/Article_en/CJFDTOTAL-ZSDZ200806018.htm
|
[2] |
E. Alpaydin, Introduction to Machine Learning. 3rd ed. New York, NY, USA:The MIT Press, 2014.
|
[3] |
S. H. Teng, H. L. Du, N. Q. Wu, W. Zhang, and J. Y. Su, "A cooperative network intrusion detection based on fuzzy SVMs, " J. Netw., vol. 5 no. 4, pp. 475-483, Jan. 2010. http://www.oalib.com/paper/2848007
|
[4] |
F. J. Kuang, W. H. Xu, and S. Y. Zhang, "A novel hybrid KPCA and SVM with GA model for intrusion detection, " Appl. Soft Comput., vol. 18, pp. 178-184, May 2014. http://www.sciencedirect.com/science/article/pii/S1568494614000477
|
[5] |
Y. H. Li, J. B. Xia, S. L. Zhang, J. K. Yan, X. C. Ai, and K. B. Dai, "An efficient intrusion detection system based on support vector machines and gradually feature removal method, " Expert Syst. Appl., vol. 39, no. 1, pp. 424-430, Jan. 2012. http://www.sciencedirect.com/science/article/pii/S0957417411009948
|
[6] |
S. M. H. Bamakan, H. D. Wang, Y. J. Tian, and Y. Shi, "An effective intrusion detection framework based on MCLP/SVM optimized by timevarying chaos particle swarm optimization, " Neurocomputing, vol. 199, pp. 90-102, Jul. 2016. http://www.sciencedirect.com/science/article/pii/S0925231216300510
|
[7] |
A. A. Aburomman and M. B. Reaz, "A novel SVM-kNN-PSO ensemble method for intrusion detection system, " Appl. Soft Comput., vol. 38, pp. 360-372, Jan. 2016. https://dl.acm.org/citation.cfm?id=2873974
|
[8] |
S. W. Lin, K. C. Ying, C. Y. Lee, and Z. J. Lee, "An intelligent algorithm with feature selection and decision rules applied to anomaly intrusion detection, " Appl. Soft Comput., vol. 12, no. 10, pp. 3285-3290, Oct. 2012. http://www.sciencedirect.com/science/article/pii/S1568494612002402
|
[9] |
W. Y. Feng, Q. L. Zhang, G. Z. Hu, and J. X. Huang, "Mining network data for intrusion detection through combining SVMs with ant colony networks, " Future Generation Comput. Syst., vol. 37, pp. 127-140, Jul. 2014. http://www.sciencedirect.com/science/article/pii/S0167739X13001416
|
[10] |
G. Kim, S. Lee, and S. Kim, "A novel hybrid intrusion detection method integrating anomaly detection with misuse detection, " Expert Syst. Appl., vol. 41, no. 4, pp. 1690-1700, Mar. 2014. doi: 10.1007/s00300-010-0766-3
|
[11] |
S. J. Horng, M. Y. Su, Y. H. Chen, T. W. Kao, R. J. Chen, J. L. Lai, and C. D. Perkasa, "A novel intrusion detection system based on hierarchical clustering and support vector machines, " Expert Syst. Appl., vol. 38, no. 1, pp. 306-313, Jan. 2011. http://www.sciencedirect.com/science/article/pii/S0957417410005701
|
[12] |
U. Ravale, N. Marathe, and P. Padiya, "Feature selection based hybrid anomaly intrusion detection system using K means and RBF kernel function, " Procedia Comput. Sci., vol. 45, pp. 428-435, Dec. 2015. http://www.sciencedirect.com/science/article/pii/s1877050915004172
|
[13] |
W. C. Lin, S. W. Ke, and C. F. Tsai, "CANN: An intrusion detection system based on combining cluster centers and nearest neighbors, " Knowl. -Based Syst., vol. 78, pp. 13-21, Apr. 2015. http://www.sciencedirect.com/science/article/pii/S0950705115000167
|
[14] |
A. Mitrokotsa, and C. Dimitrakakis, "Intrusion detection in MANET using classification algorithms: The effects of cost and model selection, " Ad Hoc Netw., vol. 11, no. 1, pp. 226-237, Jan. 2013. http://www.sciencedirect.com/science/article/pii/S1570870512001011
|
[15] |
S. Pastrana, A. Mitrokotsa, A. Orfila, P. Peris-Lopez, "Evaluation of classification algorithms for intrusion detection in MANETs, " Knowl. -Based Syst., vol. 36, pp. 217-225, Dec. 2012. http://www.sciencedirect.com/science/article/pii/S0950705112001876
|
[16] |
C. A. Catania, F. Bromberg, and C. G. Garino, "An autonomous labeling approach to support vector machines algorithms for network traffic anomaly detection, " Expert Syst. Appl., vol. 39, no. 2, pp. 1822-1829, Feb. 2012. https://dl.acm.org/citation.cfm?id=2048714
|
[17] |
S. H. Teng, C. Y. Zheng, H. B. Zhu, D. N. Liu, and W. Zhang, "A cooperative intrusion detection model for cloud computing networks, " Int. J. Sec. Appl., vol. 8 no. 3, pp. 107-118, May 2014. https://www.mendeley.com/research-papers/cooperative-intrusion-detection-model-cloud-computing-networks/
|
[18] |
W. Zhang, S. H. Teng, H. B. Zhu, and D. N. Liu, "A cooperative intrusion detection model based on granular computing and agent technologies, " Int. J. Agent Technol. Syst., vol. 5, no. 3, pp. 54-74, Jul. 2013. doi: 10.4018/ijats.2013070104
|
[19] |
W. Zhang, S. H. Teng, X. F. Fu, J. H. Fan, Y. Teng, and H. B. Zhu, "A cooperative intrusion detection model based on granular computing, " in Proc. 17th Int. Conf. Computer Supported Cooperative Work in Design, Whistler, BC, Canada, 2013, pp. 325-331. http://ieeexplore.ieee.org/document/6580983/
|
[20] |
H. B. Zhu and M. C. Zhou, "Efficient role transfer based on KuhnMunkres algorithm, " IEEE Trans. Syst. Man Cybern. A Syst. Hum., vol. 42 no. 2 pp. 491-496, Mar. 2012. http://ieeexplore.ieee.org/document/5942193/
|
[21] |
H. B. Zhu, W. Zhang, Y. Wang, J. X. Zhu, D. N. Liu, and S. H. Teng, "A role-permission assignment method of RBAC involved conflicting constraints under E-CARGO, " Int. J. Cognit. Inf. Nat. Intell., vol. 9 no. 4, pp. 49-64, Oct. 2015. https://econpapers.repec.org/article/iggjcini0/v_3a9_3ay_3a2015_3ai_3a4_3ap_3a49-64.htm
|
[22] |
H. B. Zhu, D. N. Liu, S. Q. Zhang, Y. Zhu, L. Y. Teng, and S. H. Teng, "Solving the Many to Many assignment problem by improving the Kuhn-Munkres algorithm with backtracking, " Theor. Comput. Sci., vol. 618, no. C, pp. 30-41, Mar. 2016. http://www.sciencedirect.com/science/article/pii/S0304397516000037
|
[23] |
X. J. Zhu. "Anomaly detection through statistics-based machine learning for computer networks, " Ph. D. dissertation, Univ. Arizona, Arizona, USA, 2006. http://www.openthesis.org/documents/Anomaly-Detection-Through-Statistics-Based-107307.html
|