NNCS: Randomization and Informed Search for Novel Naval Cyber Strategies

  • Stuart H. Rubin
  • Thouraya Bouabana-Tebibel
Part of the Studies in Computational Intelligence book series (SCI, volume 621)


Software security is increasingly a concern as cyber-attacks become more frequent and sophisticated. This chapter presents an approach to counter this trend and make software more resistant through redundancy and diversity. The approach, termed Novel Naval Cyber Strategies (NNCS), addresses how to immunize component-based software. The software engineer programs defining component rule bases using a schema-based Very High Level Language (VHLL). Chance and ordered transformation are dynamically balanced in the definition of diverse components. The system of systems is shown to be relatively immune to cyber-attacks; and, as a byproduct, yield this capability for effective component generalization. This methodology offers exponential increases in cyber security; whereas, conventional approaches can do no better than linear. A sample battle management application—including rule randomization—is provided.


Battle management Cybersecurity Heuristics Inferential reasoning Information dominance Military strategic planning Transfer learning 


  1. 1.
    Uspenskii, V.A.: Gödel’s Incompleteness Theorem, Translated from Russian. Ves Mir Publishers, Moscow (1987)Google Scholar
  2. 2.
    Gherbi, A., Charpentier, R., Couture, M.: Software diversity for future systems security. CrossTalk 24, 10–13 (2011)Google Scholar
  3. 3.
    Kfoury, A.J., Moll, R.N., Arbib, M.A.: A Programming Approach to Computability. Springer, New York (1982)CrossRefzbMATHGoogle Scholar
  4. 4.
    Song, D., Reiter, M., Forrest, S.: Taking Cues from Mother Nature to Foil Cyber Attacks. NSF PR 03-130, (2003)
  5. 5.
    Forrest, S., Somayaji, A., Ackley, D.H.: Building diverse computer systems. In:Workshop on Hot Topics in Operating Systems, pp. 57–72 (1997)Google Scholar
  6. 6.
    Ammann, P., Barnes, B.H., Jajodia, S., Sibley E.H. (eds): Computer Security, Dependability, and Assurance: from Needs to Solutions. IEEE Computer Society Press, Williamsburg (1998)Google Scholar
  7. 7.
    Fung, G.P.C., Yu, J.X., Lu, H.J., Yu, P.S.: Text classification without negative examples revisit. IEEE Trans. Knowl. Data Eng. 18(1), 6–20 (2006)CrossRefGoogle Scholar
  8. 8.
    Pan, S.J., Yang, Q.: A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22(10), 1345–1359 (2010)CrossRefGoogle Scholar
  9. 9.
    Lu, J., Behbood, V., Hao, P., Zuo, H., Xue, S., Zhang, G.: Transfer learning using computational intelligence: a survey. Knowl. Based Syst. 80, 14–23 (2015)CrossRefGoogle Scholar
  10. 10.
    Huang, J.-T., Li, J., Yu, D., Deng, L., Gong, Y.: Cross-language knowledge transfer using multilingual deep neural network with shared hidden layers. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Vancouver, Canada (2013)Google Scholar
  11. 11.
    Swietojanski, P., Ghoshal, A., Renals, S.: Unsupervised cross-lingual knowledge transfer in DNN-based LVCSR. In: IEEE Workshop on Spoken Language Technology, Miami, USA (2012)Google Scholar
  12. 12.
    Behbood, V., Lu, J., Zhang, G.: Text categorization by fuzzy domain adaptation. In: IEEE International Conference on Fuzzy Systems, Hyderabad, India (2013)Google Scholar
  13. 13.
    Ciresan, D.C., Meier, U., Schmidhuber, J.: Transfer learning for Latin and Chinese characters with deep neural networks. In: International Joint Conference on Neural Networks, Australia (2012)Google Scholar
  14. 14.
    Kandaswamy, C., Silva, L.M., Alexandre, L.A., Santos, J.M., De Sa, J.M.: Improving deep neural network performance by reusing features trained with transductive transference. In: 24th International Conference on Artificial Neural Networks, Hamburg, Germany (2014)Google Scholar
  15. 15.
    Shell, J., Coupland, S.: Towards fuzzy transfer learning for intelligent environments. Ambient Intell. 7683, 145–160 (2012)Google Scholar
  16. 16.
    Celiberto Jr., L.A., Matsuura, J.P., De Mantaras, R.L., Bianchi, R.A.C.: Using cases as heuristics in reinforcement learning: a transfer learning application. In: International Joint Conference on Artificial Intelligence, Barcelona, Spain (2011)Google Scholar
  17. 17.
    Niculescu-Mizil, A., Caruana, R.: Inductive transfer for Bayesian network structure learning. In: 11th International Conference on Artificial Intelligence and Statistics, Puerto Rico (2007)Google Scholar
  18. 18.
    Oyen, D., Lane, T.: Bayesian discovery of multiple Bayesian networks via transfer learning. In: 13th IEEE International Conference on Data Mining (ICDM), Dalla, USA (2013)Google Scholar
  19. 19.
    Behbood, V., Lu, J., Zhang, G.: Long term bank failure prediction using fuzzy refinement-based transductive transfer learning. In: IEEE International Conference on Fuzzy Systems, Taiwan (2011)Google Scholar
  20. 20.
    Behbood, V., Lu, J., Zhang, G.: Fuzzy bridged refinement domain adaptation: long-term bank failure prediction, Int. J. Comput. Intell. Appl. 12(01) (2013)Google Scholar
  21. 21.
    Behbood, V., Lu, J., Zhang, G.: Fuzzy refinement domain adaptation for long term prediction in banking ecosystem. IEEE Trans. Ind. Inform. 10(2), 1637–1646 (2014)CrossRefGoogle Scholar
  22. 22.
    Ma, Y., Luo, G., Zeng, X., Chen, A.: Transfer learning for cross-company software defect prediction. Inf. Softw. Technol. 54(3), 248–256 (2012)CrossRefGoogle Scholar
  23. 23.
    Luis, R., Sucar, L.E., Morales, E.F.: Inductive transfer for learning Bayesian networks. Mach. Learn. 79(1–2), 227–255 (2010)MathSciNetCrossRefGoogle Scholar
  24. 24.
    Shell, J.: Fuzzy transfer learning. Ph.D. thesis, De Montfort University (2013)Google Scholar
  25. 25.
    Chopra, S., Balakrishnan, S., Gopalan, R.: DLID: deep learning for domain adaptation by interpolating between domains. In: ICML Workshop on Challenges in Representation Learning, Atlanta, USA (2013)Google Scholar
  26. 26.
    Caruana, R.: Multitask learning: a knowledge-based source of inductive bias. In: Tenth International Conference of Machine Learning, MA, USA (1993)Google Scholar
  27. 27.
    Caruana, R.: Multitask learning. Mach. Learn. 28, 41–75 (1997)CrossRefGoogle Scholar
  28. 28.
    Silver, D.L., Poirier, R.: Context-sensitive MTL networks for machine lifelong learning. In: 20th Florida Artificial Intelligence Research Society Conference, Key West, USA (2007)Google Scholar
  29. 29.
    Dai, W., Xue, G., Yang, Q., Yu, Y.: Transferring naive Bayes classifiers for text classification. In: 22nd National Conference on Artificial Intelligence, Vancouver, Canada (2007)Google Scholar
  30. 30.
    Roy, D.M., Kaelbling, L.P.: Efficient Bayesian task-level transfer learning. In: International Joint Conference on Artificial Intelligence, Hyderabad, India (2007)Google Scholar
  31. 31.
    Shell, J., Coupland, S.: Fuzzy transfer learning: methodology and application. Inf. Sci. 293, 59–79 (2015)CrossRefGoogle Scholar
  32. 32.
    Koçer, B., Arslan, A.: Genetic transfer learning. Expert Syst. Appl. 37(10), 6997–7002 (2010)CrossRefGoogle Scholar
  33. 33.
    Chaitin, G.J.: Randomness and mathematical proof. Sci. Amer. 232(5), 47–52 (1975)CrossRefGoogle Scholar
  34. 34.
    Rubin, S.H.: On randomization and discovery. Inform. Sciences 177(1), 170–191 (2007)MathSciNetCrossRefzbMATHGoogle Scholar
  35. 35.
    Solomonoff, R.: A new method for discovering the grammars of phrase structure languages. In: International Conference Information Processing, pp. 285–290. UNESCO, Paris, France (1959)Google Scholar
  36. 36.
    Solomonoff, R.: A Formal Theory of Inductive Inference. Inf. Control 7, 71–22 and 224–254 (1964)Google Scholar
  37. 37.
    Honavar, V., Slutzki, G. (eds.): Grammatical Inference. Lecture Notes in Artificial Intelligence, vol. 1433. Springer, Berlin (1998)Google Scholar
  38. 38.
    Fu, K.S.: Syntactic Pattern Recognition and Applications. Prentice-Hall Advances in Computing Science and Technology Series. Prentice Hall, Englewood Cliffs (1982)Google Scholar
  39. 39.
    Rubin, S.H.: Computing with words. IEEE Trans. Syst. Man Cybern. Part B 29(4), 518–524 (1999)Google Scholar
  40. 40.
    Liang, Q., Rubin, S.H.: Randomization for testing systems of systems. In: Proceedings of the 10th IEEE International Conference Information Reuse and Integration, pp. 110–114. Las Vegas, NV, 10–12 Aug 2009Google Scholar
  41. 41.
    Bouabana-Tebibel, T., Rubin, S.H. (eds.): Integration of Reusable Systems. Springer, Switzerland (2014)Google Scholar
  42. 42.
    Kolmogorov, A.N., Uspenskii, V.A.: On the definition of an algorithm. Amer. Math. Soc. Transl. 29(2), 217–245 (1963) (in Russian, English translation)Google Scholar
  43. 43.
    Deitel, H.M.: An Introduction to Operating Systems. Prentice Hall, Inc., Upper Saddle River (1984)zbMATHGoogle Scholar
  44. 44.
    Nilsson, N.J.: Principles of Artificial Intelligence. Tioga Publishing Company, Palo Alto (1980)zbMATHGoogle Scholar
  45. 45.
    Bilinski, M.: Compiler Techniques as a Defense Against Cyber Attacks. Machine Learning Series. SSC-PAC, San Diego (2014)Google Scholar
  46. 46.
    Rubin, S.H.: Case-Based Generalization (CBG) for Increasing the Applicability and Ease of Access to Case-Based Knowledge for Predicting COAs. NC No. 101366 (2011)Google Scholar
  47. 47.
    Rubin, S.H., Lee, G., Chen, S.C.: A case-based reasoning decision support system for fleet maintenance. Naval Engineers J., NEJ-2009-05-STP-0239.R1, 1–10 (2009)Google Scholar
  48. 48.
    Rubin, S.H.: multi-level segmented case-based learning systems for multi-sensor fusion. NC No. 101451 (2011)Google Scholar
  49. 49.
    Rubin, S.H.: Multilevel constraint-based randomization adapting case-based learning to fuse sensor data for autonomous predictive analysis. NC 101614 (2012)Google Scholar
  50. 50.
    Minton, S.: Learning Search Control Knowledge: An Explanation Based Approach, vol. 61. Kluwer, New York (1988)Google Scholar
  51. 51.
    Feigenbaum, E.A., McCorduck, P.: The Fifth Generation. Addison-Wesley Publishing Company, Reading (1983)Google Scholar
  52. 52.
    Rubin, S.H.: Is the Kolmogorov complexity of computational intelligence bounded above? In: 12th IEEE International Conference Information Reuse and Integration, pp. 455–461. Las Vegas (2011)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Space and Naval Warfare Systems Center PacificSan DiegoUSA
  2. 2.LCSI LaboratoryÉcole nationale Supérieure d’InformatiqueAlgiersAlgeria

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