Advertisement

Understanding Cyber Warfare

  • Yan M. Yufik
Chapter
Part of the Advances in Information Security book series (ADIS, volume 55)

Abstract

The history of computing devices goes back to the invention of the abacus in Babylonia in the sixteenth century BC. In the three and a half millennia which followed, a variety of calculating devices were introduced, some of them stunningly sophisticated but all sharing a common limitation: a device could store data only as long as programs for the data manipulation remained in the mind of the user.

Keywords

Situation Awareness Neuronal Pool Short Glass Attack Graph Psychological Space 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

This research was partially supported by Air Force contract FA8750-12-C-0190 to the Institute of Medical Cybernetics, Inc. The views and opinions expressed in this article are those of the author and do not necessarily reflect the official policy or position of any agency of the US government.

References

  1. 1.
    J. Von Neumann, The First Draft Report on the EDVAC (United States Army Ordnance Department and the University of Pennsylvania, PA, 1945)Google Scholar
  2. 2.
    R. Trost, Practical Intrusion Analysis. Prevention and Detection for the Twenty-First Century. (Addison Wesley, 2010)Google Scholar
  3. 3.
    J. Andress, S. Winterfeld, Cyber Warfare: Techniques Tactics and Tools for Security Practitioners (Syngress, Amsterdam, 2011)Google Scholar
  4. 4.
    S. Cheung, U. Lindqvist, M.W. Fong, Modeling Multistep Cyber Attacks for Scenario Recognition, in Proceedings Third DARPA Information Survivability Conference and Exposition, vol. 1 Washington, DC, 2003, pp. 284–292Google Scholar
  5. 5.
    C. Tranchita, N. Hadjsaid, M. Visiteu, B. Rosel, R. Caire, ICT and power systems: An integrated approach. eds. by S. Lukszo, G. Deconinck, M.P.C. Wejnen, Securing Electricity Supply in the Cyber Age. Springer, 2010. Google Scholar
  6. 6.
    T. Ibaraki, N. Katoh, Resource Allocation Problems: Algorithmic Approaches (The MIT Press, Cambridge, 1988)zbMATHGoogle Scholar
  7. 7.
    T.B. Sheridan, Telerobotics, Automation, and Human Supervisory Control (The MIT Press, Cambridge, MA, 1992)Google Scholar
  8. 8.
    Y.M. Yufik, T.B. Sheridan, Virtual nets: framework for operator modeling and interface optimization in complex supervisory control systems. Annu. Rev. Control 20, 179–195 (1996)CrossRefGoogle Scholar
  9. 9.
    Y.M. Yufik, T.B. Sheridan, Towards a Theory of Cognitive Complexity: Helping People to Steer Complex Systems Through Uncertain and Critical Tasks Report (NASA Ames Research Center, Moffett Field, 1995)Google Scholar
  10. 10.
    Y.M. Yufik, Memory, complexity, and control in biological and artificial systems (Proc. IEEE Intell. Syst., NIST, MD, 1996), pp. 311–318Google Scholar
  11. 11.
    S.A. Reveliotis, Real-Time Management of Resource Allocation Systems: A Discrete Event Systems Approach (Springer, New York, 2004) Google Scholar
  12. 12.
    S. Jajodia, S. Noel, B. O’Berry, Topological Analysis of Network Attack Vulnerability, in Managing Cyber Threats: Issues, Approaches and Challenges, ed. by V. Kumar, J. Srivastava, A. Lazarevic (Kluwer Academic Publisher, New York, 2005)Google Scholar
  13. 13.
    C. Phillips, L.P. Swiler, A graph-based system for network vulnerability analysis, in Proceedings of the Workshop on New Security Paradigms, 1998, pp. 71–79Google Scholar
  14. 14.
    N. Idika, B. Bhargava, Extending attack graph-based security metrics and aggregating their application. IEEE Trans. Dependable Secure Comput. 9(1), 75–84 (2012)CrossRefGoogle Scholar
  15. 15.
    L. Wang, A. Singhal, S. Jajodia, Measuring overall security of network configurations using attack graphs. Data Appl. Secur. XXI 4602, 98–112 (2007)CrossRefGoogle Scholar
  16. 16.
    S. Pudar, G. Manimaran, C. Liu, PENET: A practical method and tool for integrated modeling of security attacks and countermeasures. Comput. Secur. 28, 754–771 (2009)CrossRefGoogle Scholar
  17. 17.
    T. Sommestad, M. Ekstedt, P. Johnson, A probabilistic relational model for security risk analysis. Comput. Secur. 29(6), 659–679 (2010)CrossRefGoogle Scholar
  18. 18.
    Q. Wu, Z. Shao, Network Anomaly Detection Using Time Series Analysis. Autonomic and Autonomous Systems, in Proceedings of the International Conference Networking and Services, (ICAS-ICNS, 2005) Google Scholar
  19. 19.
    Y. Yasami, S.P. Mozaffari, S. Khorsandi, Stochastic Learning Automata-Based Time Series Analysis for Network Anomaly Detection, in Proceedings of the International Conference Telecommunications, St. Peter, 2008, pp. 1–6Google Scholar
  20. 20.
    D.H. Kim, T. Lee, S.O. Jung, H.J. Lee, H. Peter, Cyber Threat Trend Analysis Model Using HMM. (2007), http://embedded.korea.ac.kr/esel/paper/international/2007/1200703.pdf
  21. 21.
    G. Dondossola, L. Pietre-Cambacedes, J. McDonald, M. Mathias Ekstedt, A. Torkilseng, Modeling of Cyber Attacks for Assessing Smart Grid Security (International Council on Large Electric Systems, Buenos Aires, Argentina, 2011)Google Scholar
  22. 22.
    S.T. Eckmann, G. Vign, R.A. Kemmerer, STATL: An attack language for state-based intrusion detection. http://www.cs.ucsb.edu/~vigna/publications/2000_eckmann_vigna_kemmerer_statl.pdf. 2000
  23. 23.
    E. Cayirci, R. Ghergherehchi, Modeling Cyber Attacks and Their Effects on Decisions Process, in Proceedings 2011 Winter Simulation Conference, 2011, ed. by S. Jain, R.R. Creasey, J. Himmelspach, K.P. White, M. Fu, pp. 2632–2641Google Scholar
  24. 24.
    S. Noel, M. Jacobs, P. Kalapa, S. Jajodia, Multiple Coordinated Views for Network Attack Graphs, in Proceedings IEEE Workshop on Visualization for Computer Security, 2005, pp. 99–106Google Scholar
  25. 25.
    K. Lakkaraju, W. Yurcik, A. Lee, NVisionIP: NetFlow Visualizations of System State for Security Situational Awareness. in Proceedings CCS Workshop on Visualization and Data Mining for Computer Security, Fairfax, VA, 2004Google Scholar
  26. 26.
    S.J. Yang, A. Stotz, J. Holsopple, M. Sudit, M. Kuhl, High level information fusion for tracking and projection of multistage cyber attacks. Inf. Fusion 10, 107–121 (2009)CrossRefGoogle Scholar
  27. 27.
    M.R. Endsley, Toward a theory of situation awareness in dynamic systems. Hum. Factors 37(1), 32–64 (1955)CrossRefGoogle Scholar
  28. 28.
    H.A. Simon, Models of Thought, vol 1–2 (Yale University Press, New Haven, 1979), p. 2Google Scholar
  29. 29.
    N. Charness, Expertise in Chess: The Balance Between Knowledge and Search, in Toward a General Theory of Expertise: Prospects and Limits, ed. by K.A. Ericsson, J. Smith (Cambridge University Press, 1991), pp. 39–63Google Scholar
  30. 30.
    J.S. Judd , Neural Network Design and the Complexity of Learning, The MIT Press, 1998.Google Scholar
  31. 31.
    M.R. Endsley, Measurement of situation awareness in dynamic systems. Hum. Factors 37(1), 65–84 (1955)CrossRefGoogle Scholar
  32. 32.
    A.D. De Groot, Though and Choice in Chess (Mouton, The Hague, Netherlands, 1965)Google Scholar
  33. 33.
    R. Penrose, A. Shimony, N. Cartwright, S. Hawking, The Large, the Small and the Human Mind, (Cambridge University Press, NY, 2007)Google Scholar
  34. 34.
    R.N. Shepard, Towards a universal law of generalization for psychological science. Sci. 237, 1317–1323, (1987)Google Scholar
  35. 35.
    J. Piaget, The Psychology of Intelligence (Harcourt Brace, New York, 1950)Google Scholar
  36. 36.
    J. Piaget, The Construction of Reality in the Child (Basic Books, New York, 1954)CrossRefGoogle Scholar
  37. 37.
    J. Piaget, Success and Understanding (Harvard University Press, Cambridge, 1978)Google Scholar
  38. 38.
    J. Piaget, The Development of Thought: Equilibration of Cognitive Structures (The Viking Press, New York, 1977)Google Scholar
  39. 39.
    J. Piaget, The Grasp of Consciousness: Action and Concept in the Young Child (Harvard University Press, Cambridge, 1976)Google Scholar
  40. 40.
    W.R. Ashby, Design for a Brain (Wiley & Sons, New York, 1953)Google Scholar
  41. 41.
    G. Kasparov, How Life imitates Chess (Bloomsbury USA, NYH, 2007)Google Scholar
  42. 42.
    M. Wertheimer, Productive Thinking (Harper, NY, 1959)Google Scholar
  43. 43.
    U.E. Siebeck, L. Litherland, G.M. Wallis, Shape learning and discrimination in reef fish. J. Exp. Biol. 212, 2113–2119 (2009)CrossRefGoogle Scholar
  44. 44.
    Y.M. Yufik, Understanding, Consciousness and Cognitive Thermodynamics, published in: Chaos, Fractals and Solitons, Special Edition Brain and Criticality, P. Grigolini and D. Chialvo (eds). 2013 (to be published)Google Scholar
  45. 45.
    Y.M. Yufik, Virtual Associative Networks: A framework for Cognitive Modeling. in Brain and Values, ed. by K.H. Pribram (Lawrence Erlbaum Associates, 1998a), pp. 109–177Google Scholar
  46. 46.
    Y.M. Yufik, D. Alkon, Modeling Adaptation and Information Fusion in the Human Nervous System (Report Office of Naval Research, Arlington, VA, 1998)Google Scholar
  47. 47.
    S.J. Landry, T.B. Sheridan, Y.M. Yufik, Cognitive grouping in air traffic control. IEEE Trans. Intel. Transp. Syst. 2, 92–101 (2001) Google Scholar
  48. 48.
    S.H. Musick, R.P. Malhotra, Sensor management for fighter applications (Sensors Directorate, WP AFB, Dayton, 2006)Google Scholar
  49. 49.
    Y.M. Yufik, Method and System for Understanding Multi Modal Data Streams. Patent pending, 2011Google Scholar
  50. 50.
    Y.M. Yufik, T.B. Sheridan, Swiss army knife and Ockham’ razor: modeling and facilitating operator’s comprehension in complex dynamic tasks. IEEE Trans. SMC 32(2), 185–198 (2001)Google Scholar
  51. 51.
    J. Kacprzyk, A. Wilbik, S. Zadrozny, An approach to the linguistic summarization of time series using a fuzzy quantifier driven aggregation. Int. J. Intell. Syst. 25(5), 411–439 (2010)zbMATHGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.Institute of Medical Cybernetics, Inc.PotomacUSA

Personalised recommendations