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Security Establishment in Cybersecurity Environment Using PSO Based Optimization

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Abstract

Cybersecurity based significant data context is considered a challenge in the research community. Machine learning approaches are considered for dealing with the big data-based security problem. Here, Particle Swarm Optimization (PSO) is used for configuring a massive amount of data. This work formulates a solution for Multi-objective problems to fulfill accuracy, computational and model complexities. A novel meta-heuristic framework for multi-objective optimization is developed for dealing with lower levels and higher-level heuristics. In the former group, various rules are generated for configuring PSO, and in the latter model, search performance to control the selection process is used for newer configurations of PSO, deal with this multi-objective function. Parento-Approximation approach is used for strengthening this framework. The proposed optimization approach can be used in cybersecurity problems like anomaly classification. The proposed model is expected to provide better results in contrast to other models.

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References

  1. Yan, Y., Qian, Y., Sharif, H., & Tipper, D. (2012). A survey on cybersecurity for smart grid communications. IEEE Communications Surveys and Tutorials, 14(4), 998–1010.

    Article  Google Scholar 

  2. Wang, W., & Lu, Z. (2013). Cybersecurity in the smart grid: Survey and challenges. Computer Networks, 57(5), 1344–1371.

    Article  Google Scholar 

  3. Bi, S., & Zhang, Y. J. (2014). Using covert topological information for defense against malicious attacks on DC state estimation. IEEE Journal on Selected Areas in Communications, 32(7), 1471–1485.

    Article  Google Scholar 

  4. Ismail, Z., Leneutre, J., Bateman, D., & Chen, L. (2014). A game-theoretical analysis of data confidentiality attacks on smart-grid AMI. IEEE Journal on Selected Areas in Communications, 32(7), 1486–1499.

    Article  Google Scholar 

  5. Guo, Y., Ten, C.-W., Hu, S., & Weaver, W. W. (2016). Preventive maintenance for advanced metering infrastructure against malware propagation. IEEE Transactions on Smart Grid, 7(3), 1314–1328.

    Article  Google Scholar 

  6. Mo, Y., Weerakkody, S., & Sinopoli, B. (2015). Physical authentication of control systems: Designing watermarked control inputs to detect counterfeit sensor outputs. IEEE Control Systems, 35(1), 93–109.

    Article  MathSciNet  Google Scholar 

  7. Lin, H., Slagell, A., Kalbarczyk, Z., Sauer, P. W., & Iyer, R. K. (2016). Runtime semantic security analysis to detect and mitigate control-related attacks in power grids. IEEE Transactions on Smart Grid, 9(1), 163–178.

    Article  Google Scholar 

  8. Fan, Y., Zhang, Z., Trinkle, M., Dimitrovski, A. D., Song, J. B., & Li, H. (2015). A cross-layer defense mechanism against GPS spoofing attacks on PMUs in smart grids. IEEE Transactions on Smart Grid, 6(6), 2659–2668.

    Article  Google Scholar 

  9. Ozay, M., Esnaola, I., Vural, F. T. Y., Kulkarni, S. R., & Poor, H. V. (2016). Machine learning methods for attack detection in the smart grid. IEEE Transactions on Neural Networks and Learning Systems, 27(8), 1773–1786.

    Article  MathSciNet  Google Scholar 

  10. Ye, Y., Li, T., Adjeroh, D., & Iyengar, S. S. (2017). A survey on malware detection using data mining techniques. ACM Computing Surveys (CSUR), 50(3), 41.

    Google Scholar 

  11. Filiol, E. (2006). Malware pattern scanning schemes secure against black-box analysis. Journal in Computer Virology, 2(1), 35–50.

    Article  Google Scholar 

  12. Filiol, E., Jacob, G., & Liard, M. L. (2007). Evaluation methodology and theoretical model for antiviral behavioural detection strategies. Journal in Computer Virology, 3(1), 23–37.

    Article  Google Scholar 

  13. Damshenas, M., Dehghantanha, A., & Mahmoud, R. (2013). A survey on malware propagation, analysis, and detection. International Journal of Cyber-Security and Digital Forensics (IJCSDF), 2(4), 10–29.

    Google Scholar 

  14. Chen, M., Mao, S., & Liu, Y. (2014). Big data: A survey. Mobile Networks and Applications, 19(2), 171–209.

    Article  Google Scholar 

  15. Gloukhov, L., Wild, C., & Reilly, D. (2015). Malware classification: Distributed data mining with spark. Association for the Advancement of Artificial Intelligence, 2015, 1–6.

    Google Scholar 

  16. Ortiz-Bayliss, J. C., Terashima-Marın, H., & Conant-Pablos, S. E. (2013). Learning vector quantization for variable ordering in constraint satisfaction problems. Pattern Recognition Letters, 34(4), 423–432.

    Article  Google Scholar 

  17. Vapnik, V. (2013). The nature of statistical learning theory. Springer.

    MATH  Google Scholar 

  18. Liu, J., Xiao, Y., Li, S., Liang, W., & Chen, C. L. P. (2012). Cyber security and privacy issues in smart grids. IEEE Communications Surveys and Tutorials, 14(4), 981–997.

    Article  Google Scholar 

  19. Huang, Y., Tang, J., Cheng, Y., Li, H., Campbell, K. A., & Han, Z. (2016). Real-time detection of false data injection in smart grid networks: An adaptive CUSUM method and analysis. IEEE Systems Journal, 10(2), 532–543.

    Article  Google Scholar 

  20. He, H., & Yun, J. (2016). Cyber-physical attacks and defences in the smart grid: A survey. IET Cyber-Physical Systems Theory and Applications, 1(1), 13–27.

    Article  MathSciNet  Google Scholar 

  21. Bao, H., Lu, R., Li, B., & Deng, R. (2016). BLITHE: Behavior rule-based insider threat detection for smart grid. IEEE Internet of Things Journal, 3(2), 190–205.

    Article  Google Scholar 

  22. Iqbal, S., et al. (2016). On cloud security attacks: A taxonomy and intrusion detection and prevention as a service. Journal of Network and Computer Applications, 74(2), 98–120.

    Article  Google Scholar 

  23. Ahmed, S., Lee, Y., Hyun, S.-H., & Koo, I. (2018). Feature selection–based detection of covert cyber deception assaults in smart grid communications networks using machine learning. IEEE Access, 6, 27518–27529.

    Article  Google Scholar 

  24. Zhao, L., & Zeng, B. (2013). Vulnerability analysis of power grids with line switching. IEEE Transactions on Power Systems, 28(3), 2727–2736.

    Article  Google Scholar 

  25. Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. In Proceedings of ICNN'95-international conference on neural networks (Vol. 4, pp. 1942–1948). IEEE.

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Acknowledgements

M. Ramakrishnan is a co-author.

Funding

UGC-RGNF (Award Number: 201516-RGNF-2015–17-SC-TAM-25217).

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Authors and Affiliations

  1. Department of Computer Applications, School of Information Technology, Madurai Kamaraj University, Madurai, Tamil Nadu, 625021, India

    J. Priyanka & M. Ramakrishnan

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  1. J. Priyanka
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  2. M. Ramakrishnan
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Contributions

PJ: contributed with full support as the technical and development. MR: contributed with full support as the guidance as well as development.

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Correspondence to J. Priyanka.

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Priyanka, J., Ramakrishnan, M. Security Establishment in Cybersecurity Environment Using PSO Based Optimization. Wireless Pers Commun 129, 1807–1828 (2023). https://doi.org/10.1007/s11277-023-10209-6

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