Answer:
Phishing
Explanation:
Phishing is one of the most common social engineering attacks in which the attacker disguises to be a trustworthy personnel in order to lure the victim or target into disclosing sensitive information such as passwords, credit card details and so on.
Phishing could be carried out via emails, telephones, or even text messages.
In this case, the attacker pretends to be an IT tech in order to get your computer configuration details which he can then use to carry out some other fraudulent acts.
Another example is in the case of someone receiving an email from their bank requesting that they need to update their records and need the person's password or credit card PIN.
Answer:
Contingency Planning Management Team (CPMT)
Explanation:
:)
Instance
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In probability theory and statistics, a shape parameter is a kind of numerical parameter of a parametric family of probability distributions.[1]
Specifically, a shape parameter is any parameter of a probability distribution that is neither a location parameter nor a scale parameter (nor a function of either or both of these only, such as a rate parameter). Such a parameter must affect the shape of a distribution rather than simply shifting it (as a location parameter does) or stretching/shrinking it (as a scale parameter does).
Contents
Estimation Edit
Many estimators measure location or scale; however, estimators for shape parameters also exist. Most simply, they can be estimated in terms of the higher moments, using the method of moments, as in the skewness (3rd moment) or kurtosis (4th moment), if the higher moments are defined and finite. Estimators of shape often involve higher-order statistics (non-linear functions of the data), as in the higher moments, but linear estimators also exist, such as the L-moments. Maximum likelihood estimation can also be used.
Examples Edit
The following continuous probability distributions have a shape parameter:
Beta distribution
Burr distribution
Erlang distribution
ExGaussian distribution
Exponential power distribution
Fréchet distribution
Gamma distribution
Generalized extreme value distribution
Log-logistic distribution
Inverse-gamma distribution
Inverse Gaussian distribution
Pareto distribution
Pearson distribution
Skew normal distribution
Lognormal distribution
Student's t-distribution
Tukey lambda distribution
Weibull distribution
Mukherjee-Islam distribution
By contrast, the following continuous distributions do not have a shape parameter, so their shape is fixed and only their location or their scale or both can change. It follows that (where they exist) the skewness and kurtosis of these distribution are constants, as skewness and kurtosis are independent of location and scale parameters.
Exponential distribution
Cauchy distribution
Logistic distribution
Normal distribution
Raised cosine distribution
Uniform distribution
Wigner semicircle distribution
See also Edit
Skewness
Kurtosis
Location parameter
Answer:
Option B (Static NAT) would be the correct choice.
Explanation:
- Static NAT seems to be a method of NAT methodology used to navigate as well as monitor internet usage from some kind of specific public IP address to something like a private IP address.
- Everything always allows the provision of web access to technology, repositories including network equipment inside a protected LAN with an unauthorized IP address.
Some other decisions made aren't relevant to the situation in question. So the above alternative is indeed the right one.
