Rectangular grid under disguised traffic attack

Rectangular grid under disguised traffic attack

The model consists of an 8×8 matrix of switching nodes that deliver packets to computing nodes which are attached to the matrix borders and produce and consume packets. Traffic guns are added to the model to represent traffic attacks. Simulation in CPN Tools revealed simple and dangerous traffic gun configurations. A. Zaitsev , T. R. Shmeleva, W. Retschitzegger, B. Pröll Security of grid structures under […]

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Source Code

Source Code

GUI No documentation currently available. Simulator If you are on Windows, you need a recent version of Cygwin You need a patched version of SML/NJ to get started. In some directory run bin/buildml where is the minor version you wish to install, e.g., 73. The directory created before must be added to your path Test that SML/NJ installed correctly by executing sml Then try executing […]

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Random distribution functions

Random distribution functions

Term definitions Random-number generator: A function that generates numbers that are uniformly distributed over the interval (0,1). Random-variate generator: A function that generates numbers whose probability distribution is different from that of the uniform on the interval (0,1). Random-variate generators Below is a brief summary of the random-number generators that are available. Click on a function name to see a more detailed explanation. bernoulli(p:real) : […]

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Bernoulli

Bernoulli

Function for generating values from Bernoulli distributions. Interface bernoulli(p:real) : int where 0.0<=p<=1.0. The value returned is either 0 or 1. The function returns a drawing from a Bernoulli distribution with probability p for success (i.e., success=1). It raises Bernoulli exception if p<0.0 or p>1.0. Characteristics Mean: p Variance: p(1-p) Example bernoulli(1.0/6.0) Throw a die and observe if a six was thrown. This experiment has […]

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Beta

Beta

Note: Introduced in CPN Tools 3.2.2. Interface  beta(a:real, a:real) : real where a,b>=0.0. Returns a drawing from a beta distribution with parameters a and b. Raises Beta exception, ifa<=0.0 or b<=0.0. Related pages Random distribution functions

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Binomial

Binomial

Function for generating values from binomial distributions. Interface binomial(n:int, p:real) : int where n>=1 and 0.0<=p<=1.0. This function returns a drawing from a binomial distribution with n experiments and probability p for success. It raises Binomial exception if n<1 or p<0.0 or p>1.0. Characteristics Mean: np Variance: np(1-p) Probability mass functions for binomial distributions: Example binomial (100, 1.0/6.0) Throw a die 100 times and observe […]

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Chi-square

Chi-square

Function for generating values from chi-square distributions. Interface chisq(n:int) : real where n>=1. It returns a drawing from a chi-square distribution with n degrees of freedom. The sum of the squares of n independent normally distributed random variables with mean 0.0 and standard deviation 1.0 is a chi-squared distribution with n degrees of freedom. It raises Chisq exception if n<1. Characteristics Mean: n Variance: 2n […]

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Discrete

Discrete

Function for generating values from discrete uniform distributions Interface discrete (a:int, b:int) : int where a<=b. Returns a drawing from a discrete uniform distribution between a and b (a and b included). Raises Discrete exception, if a>b. Characteristics Mean: (a+b)/2 Variance: ((b-a+1)^2-1)/12 Probability mass functions for discrete uniform distributions: Example discrete(1,6) Throwing a die has a discrete uniform distribution with parameters a=1 and b=6. The […]

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Erlang

Erlang

Interface erlang(n:int, r:real) : real where n>=1 and r>0.0. Returns a drawing from an n-Erlang distribution with intensity r. A drawing from an n-Erlang distribution can be derived by addition of n drawings from a exponential distribution. Raises Erlang exception, if n<1 or r<=0.0. Characteristics Mean: n/r Variance: n/r^2 Density functions for n-Erlang distributions: Example erlang(100,50.0) A shop gives each 100th customer a present. The […]

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Exponential

Exponential

Function for generating values from exponential distributions Interface exponential(r:real) : real where r>0.0. Gives a drawing from a exponential distribution with intensity r. Raises Exponential exception, if r<=0.0. Characteristics Mean: 1/r Variance: 1/r^2 Density functions for exponential distributions: Example exponential(1.0/4.0) Customers arrive at a post office for service. The time between two arrivals has a mean of 4 minutes. The inter-arrival time has an exponential […]

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