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Statistical Testing of PRNG: Generalized Gambler’s Ruin Problem

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Book cover Mathematical Aspects of Computer and Information Sciences (MACIS 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10693))

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Abstract

We present a new statistical test (GGRTest) which is based on the generalized gambler’s ruin problem (with arbitrary winning/losing probabilities). The test is able to detect non-uniformity of the outputs generated by the pseudo-random bit generators (PRNGs).

We also propose a new method, called BitTracker, of processing bits of a PRNG. In most of the statistical test-suites, bits are read in 31/32-bit groups. For many tests (e.g., OPERM) only a few first bits of the group are taken into account. Instead of “wasting” bits (in some statistical tests), the method takes into account every single bit of the PRNG’s output.

Authors were supported by Polish National Science Centre contract number DEC-2013/10/E/ST1/00359.

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References

  1. Akgün, M., Kavak, P., Demirci, H.: New results on the key scheduling algorithm of RC4. In: Chowdhury, D.R., Rijmen, V., Das, A. (eds.) INDOCRYPT 2008. LNCS, vol. 5365, pp. 40–52. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-89754-5_4

    Chapter  Google Scholar 

  2. AlFardan, N., Bernstein, D.J., Paterson, K.G., Poettering, B., Schuldt, J.C.N.: On the security of RC4 in TLS. In: Presented as part of the 22nd USENIX Security Symposium (USENIX Security 13), Washington, D.C., pp. 305–320. USENIX (2013)

    Google Scholar 

  3. Banik, S., Sarkar, S., Kacker, R.: Security analysis of the RC4+ stream cipher. In: Paul, G., Vaudenay, S. (eds.) INDOCRYPT 2013. LNCS, vol. 8250, pp. 297–307. Springer, Cham (2013). https://doi.org/10.1007/978-3-319-03515-4_20

    Chapter  Google Scholar 

  4. Barker, E., Kelsey, J.: DRAFT NIST Special Publication 800-90A, Rev. 1 - Recommendation for Random Number Generation Using Deterministic Random Bit Generators. Technical report, NIST (2014)

    Google Scholar 

  5. Bernstein, D.J.: The Salsa20 family of stream ciphers. In: Robshaw, M., Billet, O. (eds.) New Stream Cipher Designs. LNCS, vol. 4986, pp. 84–97. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-68351-3_8

    Chapter  Google Scholar 

  6. Brown, R.G., Eddelbuettel, D., Bauer, D.: Dieharder: a random number test suite. www.phy.duke.edu/~rgb/General/dieharder.php

  7. Ekkehard, H., Grønvik, A.: Re-seeding invalidates tests of random number generators. Appl. Math. Comput. 217(1), 339–346 (2010)

    MathSciNet  MATH  Google Scholar 

  8. Fluhrer, S., Mantin, I., Shamir, A.: Weaknesses in the key scheduling algorithm of RC4. In: Vaudenay, S., Youssef, A.M. (eds.) SAC 2001. LNCS, vol. 2259, pp. 1–24. Springer, Heidelberg (2001). https://doi.org/10.1007/3-540-45537-X_1

    Chapter  Google Scholar 

  9. Jha, S., Banik, S., Isobe, T., Ohigashi, T.: Some proofs of joint distributions of keystream biases in RC4. In: Dunkelman, O., Sanadhya, S.K. (eds.) INDOCRYPT 2016. LNCS, vol. 10095, pp. 305–321. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-49890-4_17

    Chapter  Google Scholar 

  10. Kang, M.: Efficiency test of pseudorandom number generators using random walks. J. Comput. Appl. Math. 174(1), 165–177 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  11. Kim, C., Choe, G.H., Kim, D.H.: Tests of randomness by the gambler’s ruin algorithm. Appl. Math. Comput. 199(1), 195–210 (2008)

    MathSciNet  MATH  Google Scholar 

  12. L’Ecuyer, P., Simard, R.: TestU01: a C library for empirical testing of random number generators. ACM Trans. Math. Softw. 33(4), 22-es (2007)

    Article  MathSciNet  MATH  Google Scholar 

  13. Lorek, P.: Generalized gambler’s ruin problem: explicit formulas via Siegmund duality. Methodol. Comput. Appl. Prob. 19(2), 603–613 (2017)

    Article  MathSciNet  MATH  Google Scholar 

  14. Matsumoto, M., Nishimura, T.: Mersenne twister: a 623-dimensionally equidistributed uniform pseudo-random number generator. ACM Trans. Model. Comput. Simul. 8(1), 3–30 (1998)

    Article  MATH  Google Scholar 

  15. Schuldt, J.C.N., Rivest, R.L.: Spritz—a spongy RC4-like stream cipher and hash function. Technical report (2014)

    Google Scholar 

  16. Paul, S., Preneel, B.: A new weakness in the RC4 keystream generator and an approach to improve the security of the cipher. In: Roy, B., Meier, W. (eds.) FSE 2004. LNCS, vol. 3017, pp. 245–259. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-25937-4_16

    Chapter  Google Scholar 

  17. Maitra, S., Paul, G.: Analysis of RC4 and proposal of additional layers for better security margin. In: Chowdhury, D.R., Rijmen, V., Das, A. (eds.) INDOCRYPT 2008. LNCS, vol. 5365, pp. 27–39. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-89754-5_3

    Chapter  Google Scholar 

  18. Vanhoef, M., Piessens, F.: All your biases belong to us: breaking RC4 in WPA-TKIP and TLS. In: USENIX Security Symposium (2015)

    Google Scholar 

  19. Wang, Y., Nicol, T.: On statistical distance based testing of pseudo random sequences and experiments with PHP and Debian OpenSSL. Comput. Secur. 53, 44–64 (2015)

    Article  Google Scholar 

  20. Zoltak, B.: VMPC one-way function and stream cipher. In: Roy, B., Meier, W. (eds.) FSE 2004. LNCS, vol. 3017, pp. 210–225. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-25937-4_14

    Chapter  Google Scholar 

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Correspondence to Filip Zagórski .

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Lorek, P., Słowik, M., Zagórski, F. (2017). Statistical Testing of PRNG: Generalized Gambler’s Ruin Problem. In: Blömer, J., Kotsireas, I., Kutsia, T., Simos, D. (eds) Mathematical Aspects of Computer and Information Sciences. MACIS 2017. Lecture Notes in Computer Science(), vol 10693. Springer, Cham. https://doi.org/10.1007/978-3-319-72453-9_34

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  • DOI: https://doi.org/10.1007/978-3-319-72453-9_34

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