Abstract
Aiming at feature selection in network traffic classification, a mixed feature selection algorithm based on SVM was proposed. This method uses the eigenvalues based on the ratio of detection rate and false alarm rate as the evaluation index for feature screening. First filter the noise and irrelevant features and reduce the feature dimension by using the F-score and the information gain in the filtering mode. Then, based on the selected cross-features subsets, use the sequential back search algorithm in the wrapping mode select the optimal feature subset in combination with SVM. The simulation results show that the optimal feature subset selected by this algorithm has good classification ability, and it also has better performance in reducing modeling time and testing time.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Zhang, Y., Yang, A., Xiong, C., et al.: Feature selection using data envelopment analysis. Knowl.-Based Syst. 64(64), 70–80 (2014)
Zhao, Y., Zhang, Y., Tong, W, et al.: An improved feature selection algorithm based on MAHALANOBIS distance for network intrusion detection. In: International Conference on Sensor Network Security Technology and Privacy Communication System, pp. 69–73. IEEE (2013)
Tesfahun, A., Bhaskari, D.L.: Intrusion detection using random forests classifier with SMOTE and feature reduction. In: International Conference on Cloud and Ubiquitous Computing and Emerging Technologies, pp. 127–132. IEEE (2014)
Araújo, N.V.D.S., Oliveira, R.D., Ferreira, E.T. et al.: Kappa-fuzzy ARTMAP: a feature selection based methodology to intrusion detection in computer networks. In: IEEE International Conference on Trust, Security and Privacy in Computing and Communications, pp. 271–276. IEEE (2013)
Balamurugan, S.A.A., Rajaram, R.: Effective and efficient feature selection for large-scale data using Bayes’ theorem. Int. J. Autom. Comput. 6(01), 62–71 (2009)
Mahalingam, P.R., Vivek, S.: Predicting financial savings decisions using sigmoid function and information gain ratio. Procedia Comput. Sci. 93, 19–25 (2016)
Maldonado, S., Weber, R.: A wrapper method for feature selection using support vector machines. Inf. Sci. 179(13), 2208–2217 (2009)
KDDCup99KDDdataset[EB/OL]. http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html (2011)
Peng, L., Fang, W.: Heterogeneity of inferring reputation of cooperative behaviors for the prisoners’ dilemma game. Phys. A: Stat. Mech. Appl. 433, 367–378 (2015)
Lipson, J., Vuong, S.: A semantics-based routing scheme for grid resource discovery. In: E-Science: First International Conference on E-Science and GridComputing, pp. 58–70, 90 (2005)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Lei, H., Gao, X. (2019). Mixed Feature Selection Method Based on SVM. In: Abawajy, J., Choo, KK., Islam, R., Xu, Z., Atiquzzaman, M. (eds) International Conference on Applications and Techniques in Cyber Security and Intelligence ATCI 2018. ATCI 2018. Advances in Intelligent Systems and Computing, vol 842. Springer, Cham. https://doi.org/10.1007/978-3-319-98776-7_133
Download citation
DOI: https://doi.org/10.1007/978-3-319-98776-7_133
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-98775-0
Online ISBN: 978-3-319-98776-7
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)