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Abstract

The security precaution of enterprise information resource database is a problem that enterprises pay close attention to extensively. In this paper, intrusion detection technology was studied, and a hybrid genetic algorithm which combined genetic algorithm with Back Propagation (BP) neural network was developed. The algorithm was tested using KDD CUP 99 data set. The results showed that the convergence effect of the hybrid genetic algorithm was good, the detection rate of the algorithm for different attacks was higher than 80%, and the accuracy rate was over 90%. The detection rate, false alarm rate, accuracy rate and detection time of the hybrid genetic algorithm were 91.36%, 6.72%, 92.24%, and 0.34 s respectively, suggesting a better detection performance. The hybrid genetic algorithm also had an accuracy rate of 98.42% in the practical application in the information resource database of an enterprise in Guizhou, China. The hybrid genetic algorithm developed in this study has a good performance in intrusion detection and has great values for the security protection of enterprise information resource database.

Keywords

Genetic algorithm big data network security intrusion detection

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References

Liu

Q.L.

and Wang

X.Q.

, The design of the enterprise network security solutions, Advanced Materials Research 1044–1045 (2014), 1397–1400.

Lorenz

et al., An SDN/NFV-enabled enterprise network architecture offering fine-grained security policy enforcement, IEEE Communications Magazine (99) (2017), 2–8.

W.J.

F.Q.

and Lu

H.F.

, Network security management system based on digital China, Applied Mechanics and Materials 568–570 (2014), 1384–1388.

Xuan

, Survey of network security situation awareness and key technologies, Electronic Test 269 (2017), 3281–3286.

Qian

Wang

Y.P.

and Li

H.X.

, The network security system research based on intrusion detection, Applied Mechanics and Materials 596 (2014), 888–891.

Zuech

Khoshgoftaar

T.M.

and Wald

, Intrusion detection and big heterogeneous data: a survey, Journal of Big Data 2(1) (2015), 3.

Kuang

and Zhang

, A novel hybrid KPCA and SVM with GA model for intrusion detection, Applied Soft Computing Journal 18(C) (2014), 178–184.

Shamshirband

et al., D-FICCA: a density-based fuzzy imperialist competitive clustering algorithm for intrusion detection in wireless sensor networks, Measurement 55(9) (2014), 212–226.

Hosseini

B.S.M.

et al., A new intrusion detection approach using PSO based multiple criteria linear programming, Procedia Computer Science 55 (2015), 23-1-237.

10.

and Zheng

S.H.

, Intrusion detection model with twin support vector machines, Journal of Shanghai Jiaotong University 19(4) (2014), 448–454.

11.

Cui

G.C.

and Liu

B.T.

, Research and implementation of intrusion detection based genetic algorithm, Applied Mechanics and Materials 631–632 (2014), 946–951.

12.

, The research and application of multi-firewall technology in enterprise network security, Computer Applications & Software 9(5) (2015), 153–162.

13.

Desale

K.S.

and Ade

, Genetic algorithm based feature selection approach for effective intrusion detection system, in: International Conference on Computer Communication and Informatics, 2015, pp. 1–6.

14.

Jiang

, A corporate computer network security system design and implementation, Advanced Materials Research 1044–1045 (2014), 965–967.

15.

Feng

et al., Mining, the network data for intrusion detection through combining SVMs with ant colony networks, Journal of Future Generation Computer Systems 37(7) (2014), 127–140.

16.

Buczak

A.L.

and Guven

, A survey of data mining and machine learning methods for cyber security intrusion detection, IEEE Communications Surveys & Tutorials 18(2) (2017), 1153–1176.

17.

Kang

M.J.

and Kang

J.W.

, A novel intrusion detection method using deep neural network for in-vehicle network security, in: Vehicular Technology Conference, 2016, pp. 1–5.

18.

Pawar

S.N.

and Bichkar

, Genetic algorithm with variable length chromosomes for network intrusion detection, International Journal of Automation & Computing 12(3) (2015), 337–342.

Security precautionary technology for enterprise information resource database based on genetic algorithm in age of big data

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