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Training Artificial Immune Networks as Standalone Generative Models for Realistic Data Synthesis

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Intelligent Information Processing XII (IIP 2024)

Part of the book series: IFIP Advances in Information and Communication Technology ((IFIPAICT,volume 703))

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Abstract

In recent years, generative modelling has become a significant area of computer science research and artificial intelligence. This has been primarily due to the fact that generative models are useful in addressing the class imbalance problem inherent in some datasets. By generating synthetic data samples for underrepresented classes with a decent amount of variation through random noise, classification models could be trained more efficiently. The popularity of generative models was also increased by the prospect of being able to generate previously non-existent samples of images, audio and video for other creative tasks not related to addressing the class imbalance in datasets. This paper presents exploratory research to train an artificial immune network as a standalone generative model (called a generative adversarial artificial immune network, or GAAINet) using purely immunological computation concepts, such as antibody affinity, clonal selection and hypermutation. Experimental results show that the resulting generator artificial immune network could generate human-recognisable synthetic handwritten digits without any prior knowledge of the MNIST handwritten digits dataset.

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References

  1. Lim, W.M., Gunasekara, A., Pallant, J.L., Pallant, J.I., Pechenkina, E.: Generative AI and the future of education: Ragnar¨ok or reformation? A paradoxical perspective from management educators. Int. J. Manage. Educ. 21(2), 100790 (2023). https://doi.org/10.1016/j.ijme.2023.100790

    Article  Google Scholar 

  2. Cao, Y., et al.: A comprehensive survey of AI-generated content (AIGC): a history of generative AI from GAN to ChatGPT (2023). https://doi.org/10.48550/arXiv.2303.04226

  3. Doersch, C.: Tutorial on variational autoencoders. arXiv:1606.05908 (2021)

  4. Goodfellow, I., et al.: Generative adversarial networks. Commun. ACM. ACM 63(11), 139–144 (2020). https://doi.org/10.1145/3422622

    Article  MathSciNet  Google Scholar 

  5. Ghojogh, B., Ghodsi, A., Karray, F., Crowley, M.: Restricted boltzmann machine and deep belief network: tutorial and survey. arXiv preprint arXiv:2107.12521 (2021)

  6. Blunsom, P.: Hidden markov models. Lecture notes, August 15(18–19), 48 (2004)

    Google Scholar 

  7. Russel, S., Norvig, P.: Artificial Intelligence: A Modern Approach. Pearson (2021)

    Google Scholar 

  8. Sharrock, J., Sun, J.C.: Innate immunological memory: from plants to animals. Curr. Opin. Immunol.. Opin. Immunol. 62, 69–78 (2020). https://doi.org/10.1016/j.coi.2019.12.001

    Article  Google Scholar 

  9. Timmis, J., Neal, M., Hunt, J.: An artificial immune system for data analysis. Biosystems 55(1), 143–150 (2000). https://doi.org/10.1016/S0303-2647(99)00092-1

    Article  Google Scholar 

  10. Jerne, N.K.: Towards a network theory of the immune system. Ann. Immunol.Immunol. 125, 373–389 (1974)

    Google Scholar 

  11. Coelho, G.P., Von Zuben, F.J.: Omni-ainet: an immune-inspired approach for omni optimization. In: Bersini, H., Carneiro, J. (eds.) Artificial Immune Systems, pp. 294–308. Springer Berlin Heidelberg, Berlin, Heidelberg (2006). https://doi.org/10.1007/11823940_23

    Chapter  Google Scholar 

  12. Diana, R.O.M., de Souza, S.R., Wanner, E.F., Filho, M.F.F.: Hybrid metaheuristic for combinatorial optimization based on immune network for optimization and VNS. In: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 251–258. GECCO 2017, Association for Computing Machinery, New York, NY, USA (2017). https://doi.org/10.1145/3071178.3071269

  13. Agiza, H.N., Hassan, A.E., Salah, A.M.: An improved version of opt-aiNET algorithm (i-opt-aiNET) for function optimization. IJCSNS Int. J. Comput. Sci. Netw. Secur. 11(3), 80–85 (2011)

    Google Scholar 

  14. Souza, S.S., Romero, R., Franco, J.F.: Artificial immune networks copt-aiNET and opt- aiNET applied to the reconfiguration problem of radial electrical distribution systems. Electr. Power Syst. Res. 119, 304–312 (2015)

    Article  Google Scholar 

  15. Kanwal, S., Khan, F., Alamri, S., Dashtipur, K., Gogate, M.: Covid-opt-aiNET: a clinical decision support system for COVID-19 detection. Int. J. Imaging Syst. Technol. 32(2), 444–461 (2022)

    Article  Google Scholar 

  16. Rassam, M.A., Maarof, M.A.: Artificial immune network clustering approach for anomaly intrusion detection. J. Adv. Inf. Technol. 3(3), 147–154 (2012)

    Google Scholar 

  17. Yang, H., Guo, J., Deng, F.: Collaborative RFID intrusion detection with an artificial immune system. J. Intell. Inf. Syst.Intell. Inf. Syst. 36(1), 1–26 (2011)

    Article  Google Scholar 

  18. Xiao, X., Zhang, R.R.: A network intrusion detection model based on artificial immune. In: Advanced Materials Research. vol. 361, pp. 687–690. Trans Tech Publ (2012)

    Google Scholar 

  19. Shi, Y., Shen, H.: Unsupervised anomaly detection for network traffic using artificial immune network. Neural Comput. Appl.Comput. Appl. 34(15), 13007–13027 (2022). https://doi.org/10.1007/s00521-022-07156-x

    Article  Google Scholar 

  20. Shi, Y., Peng, X., Li, R., Zhang, Y.: Unsupervised anomaly detection for network flow using immune network based k-means clustering. In: Zou, B., Li, M., Wang, H., Song, X., Xie, W., Lu, Z. (eds.) ICPCSEE 2017. CCIS, vol. 727, pp. 386–399. Springer, Singapore (2017). https://doi.org/10.1007/978-981-10-6385-5_33

    Chapter  Google Scholar 

  21. Sithungu, S.P., Ehlers, E.M.: GAAINet: a generative adversarial artificial immune network model for intrusion detection in industrial IoT Systems. J. Adv. Inf. Technol. 13(5), 456–461 (2022)

    Google Scholar 

  22. Sinn, M., Rawat, A.: Non-parametric estimation of jensen-shannon divergence in generative adversarial network training. In: Storkey, A., Perez-Cruz, F. (eds.) Proceedings of the Twenty-First International Conference on Artificial Intelligence and Statistics. Proceedings of Machine Learning Research, vol. 84, pp. 642–651. PMLR (2018)

    Google Scholar 

  23. Kivinen, J., Williams, C.: Multiple texture boltzmann machines. In: Lawrence, N.D., Girolami, M. (eds.) Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics. Proceedings of Machine Learning Research, vol. 22, pp. 638–646. PMLR, La Palma, Canary Islands (2012)

    Google Scholar 

  24. Larsen, R., Hilger, K.B.: Probabilistic generative modelling. In: Bigun, J., Gustavsson, T. (eds.) Image Analysis, pp. 861–868. Springer Berlin Heidelberg, Berlin, Heidelberg (2003). https://doi.org/10.1007/3-540-45103-X_114

    Chapter  Google Scholar 

  25. Tan, B., Peng, F.: Unsupervised query segmentation using generative language models and wikipedia. In: Proceedings of the 17th International Conference on World Wide Web, pp. 347–356. WWW 2008, Association for Computing Machinery, New York, NY, USA (2008). https://doi.org/10.1145/1367497.1367545

  26. Tang, Y., Salakhutdinov, R., Hinton, G.: Deep lambertian networks (2012)

    Google Scholar 

  27. Salakhutdinov, R., Larochelle, H.: Efficient learning of deep boltzmann machines. In: Teh, Y.W., Titterington, M. (eds.) Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics. Proceedings of Machine Learning Research, vol. 9, pp. 693–700. PMLR, Chia Laguna Resort, Sardinia, Italy (2010)

    Google Scholar 

  28. Goodfellow, I., et al.: Generative adversarial nets. In: Ghahramani, Z., Welling, M., Cortes, C., Lawrence, N., Weinberger, K. (eds.) Advances in Neural Information Processing Systems, vol. 27. Curran Associates, Inc. (2014)

    Google Scholar 

  29. Salimans, T., et al.: Improved techniques for training GANs. In: Lee, D., Sugiyama, M., Luxburg, U., Guyon, I., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 29. Curran Associates, Inc. (2016)

    Google Scholar 

  30. Papavasileiou, E., Cornelis, J., Jansen, B.: A systematic literature review of the successors of “neuroevolution of augmenting topologies.” Evol. Comput.. Comput. 29(1), 1–73 (2021). https://doi.org/10.1162/evco_a_00282

    Article  Google Scholar 

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Authors and Affiliations

  1. University of Johannesburg, Auckland Park, Johannesburg, 2092, South Africa

    Siphesihle Philezwini Sithungu & Elizabeth Marie Ehlers

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  1. Siphesihle Philezwini Sithungu
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  2. Elizabeth Marie Ehlers
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Correspondence to Siphesihle Philezwini Sithungu or Elizabeth Marie Ehlers .

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Editors and Affiliations

  1. Chinese Academy of Sciences, Beijing, China

    Zhongzhi Shi

  2. University of Oslo, Oslo, Norway

    Jim Torresen

  3. De Montfort University, Leicester, UK

    Shengxiang Yang

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Sithungu, S.P., Ehlers, E.M. (2024). Training Artificial Immune Networks as Standalone Generative Models for Realistic Data Synthesis. In: Shi, Z., Torresen, J., Yang, S. (eds) Intelligent Information Processing XII. IIP 2024. IFIP Advances in Information and Communication Technology, vol 703. Springer, Cham. https://doi.org/10.1007/978-3-031-57808-3_20

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  • DOI: https://doi.org/10.1007/978-3-031-57808-3_20

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  • Online ISBN: 978-3-031-57808-3

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