Recent Advances in Computational Intelligence in Defense and Security

Volume 621 of the series Studies in Computational Intelligence pp 193-223


NNCS: Randomization and Informed Search for Novel Naval Cyber Strategies

  • Stuart H. RubinAffiliated withSpace and Naval Warfare Systems Center Pacific Email author 
  • , Thouraya Bouabana-TebibelAffiliated withLCSI Laboratory, École nationale Supérieure d’Informatique

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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