Properties making a chaotic system a good Pseudo Random Number Generator

We discuss two properties making a deterministic algorithm suitable to generate a pseudo random sequence of numbers: high value of Kolmogorov-Sinai entropy and high-dimensionality. We propose the multi dimensional Anosov symplectic (cat) map as a Pseudo Random Number Generator. We show what chaotic features of this map are useful for generating Pseudo Random Numbers and investigate numerically which of them survive in the discrete version of the map. Testing and comparisons with other generators are performed.Comment: 10 pages, 3 figures, new version, title changed and minor correction

Performance evaluation of an open distributed platform for realistic traffic generation

Network researchers have dedicated a notable part of their efforts to the area of modeling traffic and to the implementation of efficient traffic generators. We feel that there is a strong demand for traffic generators capable to reproduce realistic traffic patterns according to theoretical models and at the same time with high performance. This work presents an open distributed platform for traffic generation that we called distributed internet traffic generator (D-ITG), capable of producing traffic (network, transport and application layer) at packet level and of accurately replicating appropriate stochastic processes for both inter departure time (IDT) and packet size (PS) random variables. We implemented two different versions of our distributed generator. In the first one, a log server is in charge of recording the information transmitted by senders and receivers and these communications are based either on TCP or UDP. In the other one, senders and receivers make use of the MPI library. In this work a complete performance comparison among the centralized version and the two distributed versions of D-ITG is presented

Second-level NIST randomness tests for improving test reliability

Testing Random Number Generators (RNGs) is as important as designing them. Here we consider the NIST test suite SF 800-22 and we show that, as suggested by NIST itself, to reveal non-perfect generators a more in-depth analysis should be performed using the outcomes of the suite over many generated sequences. Testing these second-level statistics is not trivial and, relying on a proper model that takes into account the errors due to the approximations in the first level tests, we propose a tuning of the parameters in the simplest cases. The validity of our consideration is widely supported by experimental results on several RNG currently employed by major IT players, as well as a chaos-based RNG designed by authors

The Monte Carlo Program KoralW version 1.51 and The Concurrent Monte Carlo KoralW&YFSWW3 with All Background Graphs and First Order Corrections to W-Pair Production

The version 1.51 of the Monte Carlo (MC) program KoralW for all $e^+e^-\to f_1\bar f_2 f_3\bar f_4$ processes is presented. The most important change since the previous version 1.42 is the facility for writing MC events on the mass storage device and re-processing them later on. In the re-processing one may modify parameters of the Standard Model in order to fit them to experimental data. Another important new feature is a possibility of including complete ${\cal O}(\alpha)$ corrections to double-resonant W-pair component-processes in addition to all background (non-WW) graphs. The inclusion is done with the help of the YFSWW3 MC event generator for fully exclusive differential distributions (event-per-event). Technically, it is done in such a way that YFSWW3 runs concurrently with KoralW as a separate slave process, reading momenta of the MC event generated by KoralW and returning the correction weight to KoralW. KoralW introduces the ${\cal O}(\alpha)$ correction using this weight, and finishes processing the event (rejection due to total MC weight, hadronization, etc.). The communication between KoralW and YFSWW3 is done with the help of the FIFO facility of the UNIX/Linux operating system. This does not require any modifications of the FORTRAN source codes. The resulting Concurrent MC event generator KoralW&YFSWW3 looks from the user's point of view as a regular single MC event generator with all the standard features.Comment: 8 figures, 5 tables, submitted to Comput. Phys. Commu

Second-level testing revisited and applications to NIST SP800-22

3noThe use of second-level testing to reduce Type II errors in RNG validation was suggested from the very beginning though rarely employed in real-world cases. Yet, as security requirements become more critical and the availability of even faster RNG more commonplace, second-level testing will be key to distinguishing RNGs based on the quality of very large chunks of their output. This paper addresses some principles governing the proper design of second-level tests (i.e. how to divide available data into chunks and how to compute second-level p-values) as well as its implications on the design of the underlying basic tests. © 2007 IEEE.partially_openopenPareschi F.; Rovatti R.; Setti G.Pareschi, F.; Rovatti, R.; Setti, G