Intrusion detection based on self-adaptive differential evolution extreme learning machine with gaussian kernel

Communications in Computer and Information Science

Volumn 729, Issue , 2017, Pages 13-24

  Ku, Junhua ^a   Zheng, Bing ^a  

^a Hainan Institute of Science and Technology   (China)

Author keywords

Differential evolution extreme learning machines; Extreme learning machines; Intrusion detection; Network security; Self adaptive differential evolution extreme learning machines

Indexed keywords

EVOLUTIONARY ALGORITHMS; FEEDFORWARD NEURAL NETWORKS; INTRUSION DETECTION; KNOWLEDGE ACQUISITION; LEARNING ALGORITHMS; LEARNING SYSTEMS; MERCURY (METAL); NETWORK ARCHITECTURE; NETWORK LAYERS; OPTIMIZATION; PARALLEL ARCHITECTURES; SECURITY OF DATA;

DETECTION ACCURACY; EXTREME LEARNING MACHINE; GAUSSIAN KERNELS; INTRUSION DETECTION SYSTEMS; NETWORK-BASED SERVICES; NEURON NUMBER; SELF-ADAPTIVE DIFFERENTIAL EVOLUTIONS; SINGLE-HIDDEN LAYER FEEDFORWARD NEURAL NETWORKS;

NETWORK SECURITY;

EID: 85031418758     PISSN: 18650929     EISSN: None     Source Type: Book Series    
DOI: 10.1007/978-981-10-6442-5_2     Document Type: Conference Paper

References (27)

1. Ilgun, K., Kemmerer, R.A., Porras, P.A.: State transition analysis: a rule-based intrusion detection approach. IEEE Trans. Softw. Eng. 21(3), 181–199 (1995)
2. Ikram, S.T., Cherukuri, A.K.: Improving accuracy of intrusion detection model using PCA and optimized SVM[J]. CIT. J. Comput. Inf. Technol. 24(2), 133–148 (2016)
3. Huang, G.B., Zhu, Q.Y., Siew, C.K.: Extreme learning machine: a new learning scheme of feedforward neural networks. In: Proceedings of International Joint Conference on Neural Networks (IJCNN2004), vol. 2, no. 25–29, pp. 985–990
4. Huang, G.B., Zhu, Q.Y., Siew, C.K.: Extreme learning machine: theory and applications. Neurocomputing 70(1–3), 489–501
5. Espana-Boquera, S., Zamora-Martínez, F., Castro-Bleda, M.J., et al.: Efficient BP algorithms for general feedforward neural networks. In: International Work-Conference on the Interplay Between Natural and Artificial Computation (pp. 327–336). Springer, Heidelberg (2007)
6. Thatte, G., Mitra, U., Heidemann, J.: Parametric methods for anomaly detection in aggregate traffic. IEEE/ACM Trans. Netw. 19(2), 512–525 (2011)
7. Qin, M., Hwang, K.: Frequent episode rules for internet anomaly detection. In: Proceedings of the Network Computing and Applications, Third IEEE International Symposium. Washington, DC, USA: IEEE Computer Society, pp. 161–168 (2004)
8. He, X., Papadopoulos, C., Heidemann, J., Mitra, U., Riaz, U.: Remote detection of bottleneck links using spectral and statistical methods. Comput. Netw. 53, 279–298 (2009)
9. Streilein, W.W., Cunningham, R.K., Webster, S.E.: Improved detection of low-profile probe and denial-of-service attacks. In: Proceedings of the 2001 Workshop on Statistical and Machine Learning Techniques in Computer Intrusion Detection (2001)
10. Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20, 273–297 (1995)
11. Huang, G.-B., Wang, D.H., Lan, Y.: Extreme learning machines: a survey. Int. J. Mach. Lean. Cybern. 2(2), 107–122 (2011)
12. Tandon, G.: Weighting versus pruning in rule validation for detecting network and host anomalies. In: Proceedings of the 13th ACM SIGKDD international. ACM Press (2007)
13. Liao, Y., Vemuri, V.R.: Use of k-nearest neighbor classifier for intrusion detection. Comput. Secur. 25, 439–448 (2002)
14. Storn, R., Price, K.: Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J. Glob. Optim. 11(4), 341–359 (2004)
15. Ilonen, J., Kamarainen, J.I., Lampinen, J.: Differential evolution training algorithm for feedforward neural networks. Neural Process. Lett. 17, 93–105 (2003)
16. Subudhi, B., Jena, D.: Differential evolution and levenberg marquardt trained neural network scheme for nonlinear system identification. Neural Process. Lett. 27, 285–296 (2008)
17. Zhu, Q.-Y., Qin, A.-K., Suganthan, P.-N., Huang, G.-B.: Evolutionary extreme learning machine. Pattern Recog. 38(10), 1759–1763 (2005)
18. KDDCUPdataset: http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html (1999)
19. Mukkamala, S., Sung, A.: Detecting denial of service attacks using support vector machines. In: Proceedings of the 12th IEEE International Conference on Fuzzy Systems (2003)
20. Luo, M., Wang, L., Zhang, H., Chen, J.: A research on intrusion detection based on unsupervised clustering and support vector machine. In: Qing, S., Gollmann, D., Zhou, J. (eds.) ICICS 2003. LNCS, vol. 2836, pp. 325–336. Springer, Heidelberg (2003). doi:10.1007/978-3-540-39927-8_30
21. Kim, D.S., Park, J.S.: Network-Based Intrusion Detection with Support Vector Machines. In: Kahng, H.-K. (ed.) ICOIN 2003. LNCS, vol. 2662, pp. 747–756. Springer, Heidelberg (2003). doi:10.1007/978-3-540-45235-5_73
22. Lin, Y., Lv, F., Zhu S., Yang, M., Cour, T., Yu, K., Cao, L., Huang, T.S.: Large-scale image classification: fast feature extraction and SVM training. In: Proceedings of the 24th IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1689–1696 (2011)
23. Huang, G.-B., Chen, L., Siew, C.K.: Universal approximation using incremental constructive feedforward networks with random hidden nodes. IEEE Trans. Neural Netw. 17(4), 879–892 (2006)
24. Storn, R., Price, K.: Differential evolution-A simple and efficient heuristic for global optimization over continuous spaces. J. Global Optim. 11, 341–359 (1997)
25. Brest, J., Greiner, S., Bŏskovíc, B., Mernik, M., Žumer, V.: Self-adapting control parameters in differential evolution: A comprehensive study on numerical benchmark problems. IEEE Trans. Evol. Comput. 10, 646–657 (2006)
26. Wu, J., Cai, Z.H.: Attribute weighting via differential evolution algorithm for attribute weighted naive bayes (WNB). J. Comput. Inf. Syst. 7, 1672–1679 (2011)
27. Stolfo, S., Fan, W., Lee, W., Prodromidis, A., Chan, P.K.: Costbased modeling for fraud and intrusion detection: results from the JAM project. In: Proceedings of DARPA Information Survivability Conference and Exposition, vol. 2, pp. 130–144 (2002)