The National Strategy for Homeland Defense, published by the then U.S. Office of Homeland Security in July 2002, directs all U.S. government agencies to conduct vulnerability analyses of their sensitive systems. This policy applies to Department of Defense systems, including the simulation packages used to design and exercise national missile defense.
Many, if not most, of the previous vulnerability analyses of missile defense simulation platforms have utilized traditional reverse engineering techniques along with a review of all documentation and publicly available sources. These experiments have produced some useful information, though the amount of platform specific data recovered has been limited.
The use of genetic algorithms (GAs) has been shown to be an effective method of performing boundary analysis and parameter optimization. In this paper, we show how GAs can be used to extract information concerning how particular parameters affect heavily parameterized missile defense simulation system performance. This information would be very valuable to researchers as part of a greater vulnerability analysis of national missile defense software.
1. Imsand, E., A. Sachitano, J.A. Hamilton, Jr.2003. Reverse Engineering Vulnerabilities in Simulation Software. Proceedings of the 2003 Advanced Simulation Technologies Conference. San Diego, CA: Society for Modeling and Simulation International (SCS), 101–107.
2.
2. Peters, M., W. Chatam, J.A. Hamilton, Jr.2003. Simulation Exploitation Using Open Source Information. Proceedings of the 2003 Advanced Simulation Technologies Conference. San Diego, CA: Society for Modeling and Simulation International (SCS), 114–118.
3.
3. Nguyen, B.2002. An Agent-Based Vulnerability Assessment System Intended for Tactical Digitized Networks. MILCOM: Proceedings of the 2002 Conference. The Boeing Corporation, Vol. 2, 1481–1484.
4.
4. Conner, M., C. Patel, M. Little.1999. Genetic Algorithm/Artificial Life Evolution of Security Vulnerability Agents. Proceedings of the 1999 MILCOM Conference. The Boeing Corporation, Vol. 1, 739–743.
5.
5. Schultz, A., J. Grefenstette, K. DeJong.1995. Learning to Break Things: Adaptive Testing of Intelligent Controllers. Handbook of Genetic Algorithms (ed. Lawrence Davis). New York: Oxford Press, G3.5.1–G3.5.10.
6.
6. Bäck, T., U. Hammel, H.P. Schwefel.1997. Evolutionary Computation: Comments on the History and Current State. IEEE Transactions on Evolutionary Computation, Vol. 1, No. 1, 3–17.
7.
7. Dozier, G., et al. 2001. An Introduction to Evolutionary Computation. Intelligent Control Systems Using Soft Computing Methodologies, (ed. Ali Zilouchian and Mohammad Jamshidi). New York: CRC Press, 365–380.
8.
8. Goldberg, D. E.1989. Genetic Algorithms in Search, Optimization & Machine Learning. Reading, MA: Addison-Wesley Publishing Company, Inc.
9.
9. Michalewicz, Z.1994. Genetic Algorithms + Data Structures = Evolution Programs (2nd edition). New York: Springer Verlag.
10.
10. Arkin, R.1998. Behavior-Based Robotics, Boston: MIT Press.
11.
11. Forrest, S., S. Hofmeyr, A. Somayaji, T. Longstaff.1996. A Sense of Self for Unix Processes. Proceedings of the IEEE Symposium on Research in Security and Privacy.
12.
12. Davis, L.1991. Handbook of Genetic Algorithms. New York: Van Nostrand Rein-hold.
13.
13. Holland, J.1975. Adaptation in Natural and Artificial Systems. Ann Arbor: The University of Michigan Press.
14.
14. Fogel, L.J., A.J. Owens, M.J. Walsh.1966. Artificial Intelligence Through Simulated Evolution. New York: Wiley Publishing.
15.
15. Kennedy, J., R. Eberhart.2001. Swarm Intelligence. Morgan Kaufmann.
16.
16. Russell, S., P. Norvig.2003. Artificial Intelligence — A Modern Approach. Upper Saddle River, NJ: Prentice Hall.
17.
17. Chaiyaratana, N., A. Szlzala.1997. Recent Developments in Evolutionary and Genetic Algorithms: Theory and Applications. Genetic Algorithms In Engineering Systems: Innovations and Applications: Proceedings of the conference in Glasgow, UK. New York: IEEE, 270–277.
18.
18. Baker, J.1987. Reducing Bias and Inefficiency in the Selection Algorithm. Proceedings of the Second International Conference on Genetic Algorithms and their Applications. Cambridge, MA: Laurence Erlbaum Associates, Inc., 14–21.
19.
19. Radcliffe, N. 1991. Genetic Neural Networks on MIMD. Ph.D. Dissertation., University of Edinburgh, Edinburgh, Scotland, United Kingdom.
20.
20. Eshelman, L., D. Schaffer.1993. Real-Coded Genetic Algorithms and Interval-Schemata. Foundations of Genetic Algorithms 2 (ed. L. Darrell Whitley). San Mateo, CA: Morgan Kaufmann Publishers, 187–202.
21.
21. Neville, M., A. Sibley.2002. Developing a Generic Genetic Algorithm. Proceedings of the 2002 ACM SIGAda International Conference. New York: The Association for Computing Machinery (ACM) Press, 45–52.
22.
22. Gottlieb, J., T. Kruse.2000. Selection in Evolutionary Algorithms for the Traveling Salesman Problem. Applied Computing: Proceedings of the 2000 ACM Conference. New York: The Association for Computing Machinery (ACM) Press, 415–421.
23.
23. Syswerda, G.1989. Uniform Crossover in Genetic Algorithms. Proceedings of the Third International Conference on Genetic Algorithms. Morgan Kaufman Publishers, 2–9.
24.
24. Moed, M., C. Stewart, R. Kelly.1991. Reducing the Search Time of a Steady State Genetic Algorithm Using the Immigration Operator. Tools for Artificial Intelligence: Proceedings of the 3rd International Conference. New York: IEEE, 500–501.
25.
25. Poirier, E., M. Ghribi, A. Kaddouri.2001. Loss Minimization Control of Induction Motor Drives Based on Genetic Algorithms. Electronic Machines and Drives: Proceedings of the 2001 Conference. New York: IEEE, 475–478.
26.
26. Sparta, Inc.2003. CAPS Database Guide. Lake Forrest, CA: Sparta, Inc.
27.
27. Sparta, Inc.2003. CAPS Training Briefing. Lake Forrest, CA: Sparta, Inc.
28.
28. Schaffer, D., et al. 1989. A Study of Control Parameters Affecting Online Performance of Genetic Algorithms for Function Optimization. Genetic Algorithms: Proceedings of the Third International Conference. San Mateo, CA: Morgan Kaufmann Publishers, Inc., 50–60.
29.
29. Digalakis, J., K. Margaritis.2000. An Experimental Study of Benchmarking Functions for Genetic Algorithms. Systems, Man, and Cybernetics: Proceedings of the 2000 Conference. New York: IEEE, Vol. 5, 3810–3815.
