NNCS: Randomization and Informed Search for Novel Naval Cyber Strategies

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.

[1]  Ray J. Solomonoff,et al.  A new method for discovering the grammars of phrase structure languages , 1959, IFIP Congress.

[2]  Jie Lu,et al.  Fuzzy bridged Refinement Domain Adaptation: Long-Term Bank Failure Prediction , 2013, Int. J. Comput. Intell. Appl..

[3]  Rich Caruana,et al.  Multitask Learning , 1997, Machine Learning.

[4]  Jie Lu,et al.  Fuzzy Refinement Domain Adaptation for Long Term Prediction in Banking Ecosystem , 2014, IEEE Transactions on Industrial Informatics.

[5]  Daniel L. Silver,et al.  Context-Sensitive MTL Networks for Machine Lifelong Learning , 2007, FLAIRS Conference.

[6]  Jethro Shell,et al.  Fuzzy transfer learning , 2013 .

[7]  Stuart Harvey Rubin Is the Kolmogorov complexity of computational intelligence bounded above? , 2011, 2011 IEEE International Conference on Information Reuse & Integration.

[8]  A. J. Kfoury,et al.  A Programming Approach to Computability , 1982, Texts and Monographs in Computer Science.

[9]  Qiang Yang,et al.  Transferring Naive Bayes Classifiers for Text Classification , 2007, AAAI.

[10]  Luis Enrique Sucar,et al.  Inductive transfer for learning Bayesian networks , 2010, Machine Learning.

[11]  Leslie Pack Kaelbling,et al.  Efficient Bayesian Task-Level Transfer Learning , 2007, IJCAI.

[12]  Abdelouahed Gherbi,et al.  Software Diversity for Future Systems Security , 2011 .

[13]  Steve Renals,et al.  Unsupervised cross-lingual knowledge transfer in DNN-based LVCSR , 2012, 2012 IEEE Spoken Language Technology Workshop (SLT).

[14]  Jie Lu,et al.  Long term bank failure prediction using Fuzzy Refinement-based Transductive Transfer learning , 2011, 2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2011).

[15]  Stuart Harvey Rubin On randomization and discovery , 2007, Inf. Sci..

[16]  STEVEN MINTON,et al.  A reply to Zito-Wolf's book review ofLearning search control knowledge: An explanation-based approach , 2004, Machine Learning.

[17]  David H. Ackley,et al.  Building diverse computer systems , 1997, Proceedings. The Sixth Workshop on Hot Topics in Operating Systems (Cat. No.97TB100133).

[18]  Luís A. Alexandre,et al.  Improving Deep Neural Network Performance by Reusing Features Trained with Transductive Transference , 2014, ICANN.

[19]  Qiang Yang,et al.  A Survey on Transfer Learning , 2010, IEEE Transactions on Knowledge and Data Engineering.

[20]  Sumit Chopra,et al.  DLID: Deep Learning for Domain Adaptation by Interpolating between Domains , 2013 .

[21]  S. A. Rubin Computing with words , 1999, IEEE Trans. Syst. Man Cybern. Part B.

[22]  Ray J. Solomonoff,et al.  A Formal Theory of Inductive Inference. Part II , 1964, Inf. Control..

[23]  Rich Caruana,et al.  Inductive Transfer for Bayesian Network Structure Learning , 2007, ICML Unsupervised and Transfer Learning.

[24]  Reinaldo A. C. Bianchi,et al.  Using Cases as Heuristics in Reinforcement Learning: A Transfer Learning Application , 2011, IJCAI.

[25]  Simon Coupland,et al.  Towards Fuzzy Transfer Learning for Intelligent Environments , 2012, AmI.

[26]  G. Chaitin Randomness and Mathematical Proof , 1975 .

[27]  Philip S. Yu,et al.  Text classification without negative examples revisit , 2006, IEEE Transactions on Knowledge and Data Engineering.

[28]  Jie Lu,et al.  Text categorization by fuzzy domain adaptation , 2013, 2013 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE).

[29]  Guangchun Luo,et al.  Transfer learning for cross-company software defect prediction , 2012, Inf. Softw. Technol..

[30]  Ahmet Arslan,et al.  Genetic transfer learning , 2010, Expert Syst. Appl..

[31]  Wendy Larson,et al.  The Fifth Generation , 2011 .

[32]  Yifan Gong,et al.  Cross-language knowledge transfer using multilingual deep neural network with shared hidden layers , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

[33]  Peng Hao,et al.  Transfer learning using computational intelligence: A survey , 2015, Knowl. Based Syst..

[34]  King-Sun Fu,et al.  Syntactic Pattern Recognition And Applications , 1968 .

[35]  Simon Coupland,et al.  Fuzzy Transfer Learning: Methodology and application , 2015, Inf. Sci..

[36]  Rich Caruana,et al.  Multitask Learning: A Knowledge-Based Source of Inductive Bias , 1993, ICML.

[37]  Thouraya Bouabana-Tebibel,et al.  Integration of Reusable Systems [extended versions of the best papers which were presented at IEEE International Conference on Information Reuse and Integration and IEEE International Workshop on Formal Methods Integration, San Francisco, CA, USA, August 2013] , 2014, IRI.

[38]  Stuart Harvey Rubin,et al.  Randomization for testing systems of systems , 2009, 2009 IEEE International Conference on Information Reuse & Integration.

[39]  Nils J. Nilsson,et al.  Principles of Artificial Intelligence , 1980, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[40]  Jürgen Schmidhuber,et al.  Transfer learning for Latin and Chinese characters with Deep Neural Networks , 2012, The 2012 International Joint Conference on Neural Networks (IJCNN).

[41]  Terran Lane,et al.  Bayesian Discovery of Multiple Bayesian Networks via Transfer Learning , 2013, 2013 IEEE 13th International Conference on Data Mining.

[42]  Harvey M. Deitel,et al.  An introduction to operating systems , 1984 .