Evolving Cryptographic Pseudorandom Number Generators

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• 关键词：Random number generators ; Pseudorandomness ; Cryptography ; Cartesian Genetic Programming ; Statistical tests
• 刊名：Lecture Notes in Computer Science
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：9921
• 期：1
• 页码：613-622
• 全文大小：266 KB
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  9.Koza, J.R.: Evolving a computer program to generate random numbers using the genetic programming paradigm. In: Proceedings of the Fourth International Conference on Genetic Algorithms, pp. 37–44. Morgan Kaufmann (1991)
  10.Hernandez, J., Seznec, A., Isasi, P.: On the design of state-of-the-art pseudorandom number generators by means of genetic programming. In: Congress on Evolutionary Computation, CEC2004, vol. 2, pp. 1510–1516, June 2004
  11.Warren, H.S.: Hacker’s Delight. Addison-Wesley Longman Publishing Co., Inc., Boston (2002)
  12.Miller, J.F., Thomson, P.: Cartesian genetic programming. In: Poli, R., Banzhaf, W., Langdon, W.B., Miller, J., Nordin, P., Fogarty, T.C. (eds.) EuroGP 2000. LNCS, vol. 1802, pp. 121–132. Springer, Heidelberg (2000)CrossRef
  13.Tian, X., Benkrid, K.: Mersenne twister random number generation on FPGA, CPU and GPU. In: NASA/ESA Conference on Adaptive Hardware and Systems, AHS 2009, pp. 460–464, July 2009
  14.Picek, S., Jakobovic, D., Miller, J.F., Batina, L., Cupic, M.: Cryptographic boolean functions: one output, many design criteria. Appl. Soft Comput. 40, 635–653 (2016)CrossRef
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• 作者单位：Stjepan Picek (19)
  Dominik Sisejkovic (20)
  Vladimir Rozic (19)
  Bohan Yang (19)
  Domagoj Jakobovic (20)
  Nele Mentens (19)
  
  19. KU Leuven, ESAT/COSIC and iMinds, Kasteelpark Arenberg 10, bus 2452, 3001, Leuven-Heverlee, Belgium
  20. Faculty of Electrical Engineering and Computing, University of Zagreb, Zagreb, Croatia
  
• 丛书名：Parallel Problem Solving from Nature ¨C PPSN XIV
• ISBN：978-3-319-45823-6
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349
• 卷排序：9921

文摘

Random number generators (RNGs) play an important role in many real-world applications. Besides true hardware RNGs, one important class are deterministic random number generators. Such generators do not possess the unpredictability of true RNGs, but still have a widespread usage. For a deterministic RNG to be used in cryptography, it needs to fulfill a number of conditions related to the speed, the security, and the ease of implementation. In this paper, we investigate how to evolve deterministic RNGs with Cartesian Genetic Programming. Our results show that such evolved generators easily pass all randomness tests and are extremely fast/small in hardware.