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An efficient surrogate-assisted Taguchi salp swarm algorithm and its application for intrusion detection

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Abstract

The meta-heuristic algorithms require a lot of fitness calculations to get good enough solutions, which constitutes an obstacle to solving computationally complex practical problems. Recently, it has been found that surrogate-assisted meta-heuristic algorithms show potential in solving expensive complex optimization problems. This paper proposes an efficient surrogate-assisted Taguchi salp swarm algorithm (SATSSA) to solve expensive complex optimization problems. The SATSSA uses a combination of the local surrogate-assisted model (LSAM), global surrogate-assisted model (GSAM), and k-means clustering surrogate-assisted model (KCSAM) to fit the fitness function. To enhance the prediction ability of the model, an improved salp swarm algorithm with the Taguchi method (TSSA) is proposed to update and predict the model. GSAM is mainly used to capture the entire landscape of the search space. KCSAM is designed to capture part of the search space to improve the exploration capability of the algorithm. LSAM is used to capture the contours around the optimal individual. The proposed SATSSA is compared with other four excellent algorithms on 30D, 50D, and 100D benchmark functions. In addition, SATSSA is also applied to intrusion detection. Simulation results show that SATSSA is effective in improving detection rate and reducing false alarm rate.

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The data that support the findings of this study are available from the authors, upon reasonable request.

References

  1. Ali, A., Ming, Yu., Chakraborty, S., & Iram, S. (2017). A comprehensive survey on real-time applications of WSN. Future Internet, 9(4), 77.

    Google Scholar 

  2. Prithi, S., & Sumathi, S. (2021). Automata based hybrid PSO-GWO algorithm for secured energy efficient optimal routing in wireless sensor network. Wireless Personal Communications, 117, 545–559.

    Google Scholar 

  3. Kulkarni, R. V., & Venayagamoorthy, G. K. (2010). Particle swarm optimization in wireless-sensor networks: A brief survey. IEEE Transactions on Systems, Man, and Cybernetics, 41(2), 262–267.

    Google Scholar 

  4. Aburomman, A. A., & Reaz, M. B. I. (2016). A novel SVM-kNN-PSO ensemble method for intrusion detection system. Applied Soft Computing, 38, 360–372.

    Google Scholar 

  5. Singh, A., Chatterjee, K., & Satapathy, S. C. (2022). An edge based hybrid intrusion detection framework for mobile edge computing. Complex & Intelligent Systems, 8(5), 3719–3746.

    Google Scholar 

  6. Li, Z., Miao, Q., Chaudhry, S. A., & Chen, C. M. (2022). A provably secure and lightweight mutual authentication protocol in fog-enabled social Internet of vehicles. International Journal of Distributed Sensor, 18(6), 15501329221104332.

    Google Scholar 

  7. Ayyagari, M. R., Kesswani, N., Kumar, M., & Kumar, K. (2021). Intrusion detection techniques in network environment: A systematic review. Wireless Networks, 27, 1269–1285.

    Google Scholar 

  8. Wu, T. Y., Meng, Q., Kumari, S., & Zhang, P. (2022). Rotating behind security: A lightweight authentication protocol based on IoT-enabled cloud computing environments. Sensors, 22(10), 3858.

    Google Scholar 

  9. Kumaresan, G., & Adiline, M. T. (2017). Group key authentication scheme for vanet intrusion detection (GKAVIN). Wireless Networks, 23, 935–945.

    Google Scholar 

  10. Liao, Y., & Vemuri, V. R. (2002). Use of k-nearest neighbor classifier for intrusion detection. Computers & Security, 21(5), 439–448.

    Google Scholar 

  11. Almomani, O. (2020). A feature selection model for network intrusion detection system based on PSO, GWO, FFA and GA algorithms. Symmetry, 12(6), 1046.

    Google Scholar 

  12. Otair, M., Ibrahim, O. T., Abualigah, L., Altalhi, M., & Sumari, P. (2022). An enhanced grey wolf optimizer based particle swarm optimizer for intrusion detection system in wireless sensor networks. Wireless Networks, 28(2), 721–744.

    Google Scholar 

  13. Liu, G. Y., Zhao, H. Q., Fan, F., Liu, G., Xu, Q., & Nazir, S. (2022). An enhanced intrusion detection model based on improved kNN in WSNs. Sensors, 22(4), 1407.

    Google Scholar 

  14. Aghdam, M. H., & Kabiri, P. (2016). Feature selection for intrusion detection system using ant colony optimization. International Journal of Network Security, 18(3), 420–432.

    Google Scholar 

  15. Dorigo, M., Birattari, M., & Stutzle, T. (2006). Ant colony optimization. IEEE Computational Intelligence Magazine, 1(4), 28–39.

    Google Scholar 

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

  17. Yu, Y., Xu, Y., Wang, F., Li, W., Mai, X., & Wu, H. (2021). Adsorption control of a pipeline robot based on improved PSO algorithm. Complex & Intelligent Systems, 7(4), 1797–1803.

    Google Scholar 

  18. Storn, R., & Price, K. (1997). Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization, 11(4), 341–359.

    MathSciNet  Google Scholar 

  19. Shi, L., Hu, Z., Su, Q., & Miao, Y. (2022). A modified multifactorial differential evolution algorithm with optima-based transformation. Applied Intelligence, 1–13.

  20. Whitley, D. (1994). A genetic algorithm tutorial. Statistics and Computing, 4(2), 65–85.

    Google Scholar 

  21. Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey wolf optimizer. Advances in Engineering Software, 69, 46–61.

    Google Scholar 

  22. Mirjalili, S., Gandomi, A. H., Mirjalili, S. Z., Saremi, S., Faris, H., & Mirjalili, S. M. (2017). Salp swarm algorithm: A bio-inspired optimizer for engineering design problems. Advances in Engineering Software, 114, 163–191.

    Google Scholar 

  23. Abualigah, L., Shehab, M., Alshinwan, M., & Alabool, H. (2020). Salp swarm algorithm: A comprehensive survey. Neural Computing and Applications, 32(15), 11195–11215.

    Google Scholar 

  24. Wang, C., Xu, R. Q., Ma, L., Zhao, J., Wang, L., Xie, N. G., & Cheong, K. H. (2022). An efficient salp swarm algorithm based on scale-free informed followers with self-adaption weight. Applied Intelligence, 1–33.

  25. Karaboga, D., & Akay, B. (2009). A comparative study of artificial bee colony algorithm. Applied Mathematics and Computation, 214(1), 108–132.

    MathSciNet  Google Scholar 

  26. Jiang, Q., Cui, J., Ma, Y., Wang, L., Lin, Y., Li, X., Feng, T., & Wu, Y. (2022). Improved adaptive coding learning for artificial bee colony algorithms. Applied Intelligence, 1–49.

  27. Cong, C. (2015). A coverage algorithm for WSN based on the improved PSO. In 2015 International conference on intelligent transportation, big data and smart city (pp. 12–15). IEEE.

  28. Agrawal, D., Wasim Qureshi, M. H., Pincha, P., Srivastava, P., Agarwal, S., Tiwari, V., & Pandey, S. (2020). GWO-C: Grey wolf optimizer-based clustering scheme for WSNs. International Journal of Communication, Systems, 33(8), e4344.

    Google Scholar 

  29. Wu, J., Xu, M., Liu, F. F., Huang, M., Ma, L. H., & Lu, Z. M. (2021). Solar wireless sensor network routing algorithm based on multi-objective particle swarm optimization. Journal of Information Hiding and Multimedia Signal Processing, 12(1), 1–11.

    Google Scholar 

  30. Chen, S., Wu, J., & Lu, Z. H. (2012). A cloud computing resource scheduling policy based on genetic algorithm with multiple fitness. In 2012 IEEE 12th international conference on computer and information technology (pp. 177–184). IEEE.

  31. Liu, S., Wang, H., Peng, W., & Yao, W. (2022). A surrogate-assisted evolutionary feature selection algorithm with parallel random grouping for high-dimensional classification. IEEE Transactions on Evolutionary Computation, 26(5), 1087–1101.

    Google Scholar 

  32. Gu, S., Cheng, R., & Jin, Y. (2018). Feature selection for high-dimensional classification using a competitive swarm optimizer. Soft Computing, 3, 811–822.

    Google Scholar 

  33. Al-Yaseen, W. L., Idrees, A. K., & Almasoudy, F. H. (2022). Wrapper feature selection method based differential evolution and extreme learning machine for intrusion detection system. Pattern Recognition, 132, 108912.

    Google Scholar 

  34. Zhang, F., Mei, Y., Nguyen, S., Zhang, M., & Tan, K. C. (2021). Surrogate-assisted evolutionary multitask genetic programming for dynamic flexible job shop scheduling. IEEE Transactions on Evolutionary Computation, 25(4), 651–665.

    Google Scholar 

  35. Denkena, B., Schinkel, F., Pirnay, J., & Wilmsmeier, S. (2021). Quantum algorithms for process parallel flexible job shop scheduling. CIRP Journal of Manufacturing Science and Technology, 33, 100–114.

    Google Scholar 

  36. Gu, Q., Wang, Q., Xiong, N. N., Jiang, S., & Chen, L. (2021). Surrogate-assisted evolutionary algorithm for expensive constrained multi-objective discrete optimization problems. Complex & Intelligent Systems, 1–20.

  37. Liu, N., Pan, J. S., Chu, S. C., & Lai, T. (2022). A surrogate-assisted bi-swarm evolutionary algorithm for expensive optimization. Applied Intelligence, 1–24.

  38. Jin, Y. (2011). Surrogate-assisted evolutionary computation: Recent advances and future challenges. Swarm and Evolutionary Computation, 1(2), 61–70.

    Google Scholar 

  39. Zhao, Y., Zhao, J., Zeng, J., & Tan, Y. (2022). A two-stage infill strategy and surrogate-ensemble assisted expensive many-objective optimization. Complex & Intelligent Systems, 1–17.

  40. Pan, J. S., Liu, N., Chu, S. C., & Lai, T. (2021). An efficient surrogate-assisted hybrid optimization algorithm for expensive optimization problems. Information Sciences, 561, 304–325.

    Google Scholar 

  41. Yu, H., Tan, Y., Zeng, J., Sun, C., & Jin, Y. (2018). Surrogate-assisted hierarchical particle swarm optimization. Information Sciences, 454, 59–72.

    MathSciNet  Google Scholar 

  42. Loshchilov, I., Schoenauer, M., & Sebag, M. (2010) Comparison-based optimizers need comparison-based surrogates. In International conference on parallel problem solving from nature (pp. 364–373). Springer.

  43. Chugh, T., Jin, Y., Miettinen, K., Hakanen, J., & Sindhya, K. (2016). A surrogate-assisted reference vector guided evolutionary algorithm for computationally expensive many-objective optimization. IEEE Transactions on Evolutionary Computation, 22(1), 129–142.

    Google Scholar 

  44. Cho, S., Kim, M., Lyu, B., & Moon, I. (2021). Optimization of an explosive waste incinerator via an artificial neural network surrogate model. Chemical Engineering Journal, 407, 126659.

    Google Scholar 

  45. Zhou, Z., Ong, Y. S., Nguyen, M. H, & Lim, D. (2005) A study on polynomial regression and gaussian process global surrogate model in hierarchical surrogate-assisted evolutionary algorithm. In 5 IEEE congress on evolutionary computation. (pp. 2832–2839). IEEE.

  46. Díaz-Manríquez, A., Toscano-Pulido, G., & Gómez-Flores, W. (2011). On the selection of surrogate models in evolutionary optimization algorithms. In 2011 IEEE congress of evolutionary computation (CEC) (pp. 2155–2162). IEEE.

  47. Hu, P., Pan, J. S., Chu, S. C., & Sun, C. (2022). Multi-surrogate assisted binary particle swarm optimization algorithm and its application for feature selection. Applied Soft Computing, 121, 108736.

    Google Scholar 

  48. Sun, C., Zeng, J., Pan, J. S., Xue, S., & Jin, Y. (2013). A new fitness estimation strategy for particle swarm optimization. Information Sciences, 221, 355–370.

    MathSciNet  Google Scholar 

  49. Chu, S. C., Du, Z. G., Peng, Y. J., & Pan, J. S. (2021). Fuzzy hierarchical surrogate assists probabilistic particle swarm optimization for expensive high dimensional problem. Knowledge-Based Systems, 220, 106939.

    Google Scholar 

  50. Jin, Y., Olhofer, M., & Sendhoff, B. (2002). A framework for evolutionary optimization with approximate fitness functions. IEEE Transactions on Evolutionary Computation, 6(5), 481–494.

    Google Scholar 

  51. Ren, Z., Sun, C., Tan, Y., Zhang, G., & Qin, S. (2021). A bi-stage surrogate-assisted hybrid algorithm for expensive optimization problems. Complex & Intelligent Systems, 7(3), 1391–1405.

    Google Scholar 

  52. Hardy, R. L. (1971). Multiquadric equations of topography and other irregular surfaces. Journal of Geophysical Research, 76(8), 1905–1915.

    Google Scholar 

  53. Pan, J. S., Tian, A. Q., Snášel, V., Kong, L., & Chu, S. C. (2022). Maximum power point tracking and parameter estimation for multiple-photovoltaic arrays based on enhanced pigeon-inspired optimization with taguchi method. Energy, 251, 123863.

    Google Scholar 

  54. Regis, R. G. (2014). Particle swarm with radial basis function surrogates for expensive black-box optimization. Journal of Computational Science, 5(1), 12–13.

    MathSciNet  Google Scholar 

  55. Sun, C., Jin, Y., Cheng, R., Ding, J., & Zeng, J. (2017). Surrogate-assisted cooperative swarm optimization of high-dimensional expensive problems. IEEE Transactions on Evolutionary Computation, 21(4), 644–660.

    Google Scholar 

  56. Tavallaee, M., Bagheri, E., Lu, W., & Ghorbani, A. A. (2009). A detailed analysis of the kdd cup 99 data set. In 2009 IEEE symposium on computational intelligence for security and defense applications (pp. 1–6). IEEE.

  57. McHugh, J. (2000). Testing intrusion detection systems: a critique of the 1998 and 1999 darpa intrusion detection system evaluations as performed by lincoln laboratory. ACM Transactions on Information and System Security, 3(4), 262–294.

    Google Scholar 

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Authors and Affiliations

  1. College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, 266590, China

    Shu-Chuan Chu, Xu Yuan, Jeng-Shyang Pan & Tsu-Yang Wu

  2. Department of Information Management, Chaoyang University of Technology, Taichung, 41349, Taiwan

    Jeng-Shyang Pan

  3. School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai, 201620, China

    Fengting Yan

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Chu, SC., Yuan, X., Pan, JS. et al. An efficient surrogate-assisted Taguchi salp swarm algorithm and its application for intrusion detection. Wireless Netw 30, 2675–2696 (2024). https://doi.org/10.1007/s11276-024-03677-6

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