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A Network Illegal Access Detection Method Based on PSO-SVM Algorithm in Power Monitoring System

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11064))

Abstract

In this paper, a network illegal access detection method based on PSO-SVM algorithm in the power monitoring system is proposed, where the particle swarm optimization (PSO) algorithm is used to optimize the parameters of support vector machine (SVM). And the proposed method is used to classify normal data flow and abnormal data flow in the network to detect whether there is illegal access behavior in the power monitoring system. Compared with the original SVM-based method and LS-SVM-based method, the proposed method not only improves the convergence speed of the network training, but also improves the accuracy rate. The proposed method can fleetly discovery and locate the illegal access behavior in the power monitoring system, which is a meaningful study in improving the security of the power monitoring system.

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References

  1. Luo, B., Xia, J.: A novel intrusion detection system based on feature generation with visualization strategy. Expert Syst. Appl. 41(9), 4139–4147 (2014)

    Article  MathSciNet  Google Scholar 

  2. Liu, C., Hsaio, W., Chang, T.: Locality sensitive K-means clustering. J. Inf. Sci. Eng. 34(1), 289–305 (2018)

    MathSciNet  Google Scholar 

  3. Selvakumar, J., Lakshmi, A., Arivoli, T.: Brain tumor segmentation and its area calculation in brain MR images using K-mean clustering and Fuzzy C-mean algorithm. In: International Conference on Advances in Engineering, Science and Management, pp. 186–190 (2012)

    Google Scholar 

  4. Goldberg, D.E.: Genetic algorithms in search. Optim. Mach. Learn. 13(7), 2104–2116 (1989). Addion wesley

    Google Scholar 

  5. Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. In: Joint International Conference on Artificial Neural Networks and Neural Information Processing, pp. 737–744. Springer-Verlag (2003)

    Google Scholar 

  6. Zhang, X.H., Lin, B.G.: Research on internet of things security based on support vector machines with balanced binary decision tree. Netinfo Secur. 8, 20–25 (2015)

    Google Scholar 

  7. Wang, J., Zhu, W., Zhang, W., et al.: A trend fixed on firstly and seasonal adjustment model combined with the SVR for short-term forecasting of electricity demand. Energy Policy 37(11), 4901–4909 (2009)

    Article  Google Scholar 

  8. Fukunaga, K.: Introduction to statistical pattern recognition. 60(12–1), 2133–2143 (1972). (2nd edn.)

    Google Scholar 

  9. Wang, Y.Y., Xue, J.H.: Particle swarm optimization algorithm. J. Nantong Text. Vocat. Technol. Coll. 306(3), 1369–1372 (2009)

    Google Scholar 

  10. Zhong, Y.H., Zhang, P.X.: Generalized trapezoidal decision-theoretic rough sets. Math. Pract. Theor. 6, 9 (2015)

    Google Scholar 

  11. Faria, P., Soares, J., Vale, Z., et al.: Modified particle swarm optimization applied to integrated demand response and DG resources scheduling. Trans. Smart Grid 4(1), 606–616 (2013)

    Article  Google Scholar 

  12. Esmin, A.A.A., Coelho, R.A., Matwin, S.: A review on particle swarm optimization algorithm and its variants to clustering high-dimensional data. Artif. Intell. Rev. 44(1), 23–45 (2015)

    Article  Google Scholar 

  13. Yang, J.: Particle swarm optimization algorithm based on chaos searching. Comput. Eng. Appl. 16, 69–71 (2013)

    Google Scholar 

  14. Xu, Z., Yager, R.R.: Some geometric aggregation operators based on intuitionistic fuzzy sets. Int. J. Gen Syst 35(4), 417–433 (2006)

    Article  MathSciNet  Google Scholar 

  15. Lee, W., Stolfo, S.J.: Data mining approaches for intrusion detection. In: Conference on Usenix Security Symposium, pp. 79–93. USENIX Association (1998)

    Google Scholar 

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Correspondence to Zhizhong Qiao .

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Su, Y., Zhang, W., Tao, W., Qiao, Z. (2018). A Network Illegal Access Detection Method Based on PSO-SVM Algorithm in Power Monitoring System. In: Sun, X., Pan, Z., Bertino, E. (eds) Cloud Computing and Security. ICCCS 2018. Lecture Notes in Computer Science(), vol 11064. Springer, Cham. https://doi.org/10.1007/978-3-030-00009-7_41

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  • DOI: https://doi.org/10.1007/978-3-030-00009-7_41

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-00008-0

  • Online ISBN: 978-3-030-00009-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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