Abstract
To the problem of the traditional parameters optimization algorithm may level into local optimal, a parameter optimization method crossover mutation artificial bee colony based on artificial bee colony algorithm is proposed to solve this problem and applied to intrusion detection. And introduced an improved artificial colony algorithm based on crossover mutation operator, the whole bee colony could be divided into two sub-populations according to the fitness value of colony and effectively avoid local optimum and enhance convergence speed, use standard test functions to verify the effectiveness of the algorithm. And the proposed algorithm’s performance is tested by the KDD-99 datasets, the experimental results show that this method can effective improve the classification performance of intrusion detection.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Bahri, E., Harbi, N., Huu, H.N.: A multiple classifier system using an adaptive strategy for intrusion detection. In: International Conference on Intelligent Computational Systems (ICICS), pp. 124–128 (2012)
Mao, Y., Zhou, X.-b., Pi, D.-y., et al.: Parameters selection in gene selection using Gaussian kernel support vector machines by genetic algorithm. J. Zhe Jiang Univ. Sci. 6(10), 961–973 (2005)
Kumar, A.: Parameter optimization using genetic algorithm for support vector machine-based price-forecasting model in National electricity Market. IET Gener. Transm. Distrib. IET 4(1), 36–49 (2010)
Karaboga, D.: An idea based on honey bee swarm for numerical optimization. Technical report-tr06, Erciyes university, engineering faculty, computer engineering department (2005)
Shi, X., Li, Y., Li, H., et al.: An integrated algorithm based on artificial bee colony and particle swarm optimization. In: Sixth International Conference on Natural Computation, ICNC, pp. 2586–2590 (2010)
Zhu, G., Kwong, S.: Gbest-guided artificial bee colony algorithm for numerical function optimization. Appl. Math. Comput. 217(7), 3166–3173 (2010)
Unler, A., Murat, A., Chinnam, R.B.: mr 2 PSO: a maximum relevance minimum redundancy feature selection method based on swarm intelligence for support vector machine classification. Inf. Sci. 181(20), 4625–4641 (2011)
KDD-99dataset for network-based intrusion detection systems. http://iscx.info/KDD-99
Drucker, H., Wu, D., Vipnik, V.N.: Support vector machines for spam categorization. IEEE Trans. Neural Netw. 10(5), 1048–1054 (1999)
Karaboga, D., Ozturk, C.: A novel clustering approach: Artificial Bee Colony (ABC) algorithm. Appl. Soft Comput. 11(1), 652–657 (2011)
Karaboga, D., Basturk, B.: A comparative study of artificial bee colony algorithm. Appl. Math. Comput. 214(1), 108–132 (2009)
Qin, A.K., Suganthan, P.N.: Self-adaptive differential evolution algorithm for numerical optimization. In: The 2005 IEEE Congress on Evolutionary Computation, pp. 1785–1791. IEEE (2005)
Tinoco, J.C.V., Coello, C.A.: hypDE: a hyper-heuristic based on differential evolution for solving constrained optimization problems. In: Schütze, O., Coello Coello, C.A., Tantar, A.-A., Tantar, E., Bouvry, P., Del Moral, P., Legrand, P. (eds.) EVOLVE - A Bridge Between Probability, Set Oriented Numerics, and Evolutionary Computation II. AISC, vol. 175, pp. 267–282. Springer, Heidelberg (2012)
Mezher, M.A., Abbod, M.F.: Genetic folding for solving multicast SVM problems. Applied Intelligence 41(2), 464–472 (2014)
Acknowledgments
This research is supported in part by the National Natural Science Foundation of China under Grant Nos. 61262072.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Liu, M., Yang, X., Huang, F., Fu, Y. (2015). Research of CMABC Algorithm in Intrusion Detection. In: Wang, G., Zomaya, A., Martinez, G., Li, K. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2015. Lecture Notes in Computer Science(), vol 9532. Springer, Cham. https://doi.org/10.1007/978-3-319-27161-3_28
Download citation
DOI: https://doi.org/10.1007/978-3-319-27161-3_28
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-27160-6
Online ISBN: 978-3-319-27161-3
eBook Packages: Computer ScienceComputer Science (R0)