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NEW QUESTION: 1
Which of the following statements pertaining to the Bell-LaPadula is TRUE if you are NOT making use of the strong star property?
A. It allows "read up."
B. It addresses management of access controls.
C. It addresses covert channels.
D. It allows "write up."
Answer: D
Explanation:
Bell-LaPadula Confidentiality Model10 The Bell-LaPadula model is perhaps the most well-known and significant security model, in addition to being one of the oldest models used in the creation of modern secure computing systems. Like the Trusted Computer System
Evaluation Criteria (or TCSEC), it was inspired by early U.S. Department of Defense security
policies and the need to prove that confidentiality could be maintained. In other words, its primary
goal is to prevent disclosure as the model system moves from one state (one point in time) to
another.
When the strong star property is not being used it means that both the * property and the Simple
Security Property rules would be applied.
The Star (*) property rule of the Bell-LaPadula model says that subjects cannot write down, this
would compromise the confidentiality of the information if someone at the secret layer would write
the object down to a confidential container for example.
The Simple Security Property rule states that the subject cannot read up which means that a
subject at the secret layer would not be able to access objects at Top Secret for example.
You must remember: The model tells you about are NOT allowed to do. Anything else would be
allowed. For example within the Bell LaPadula model you would be allowed to write up as it does
not compromise the security of the information. In fact it would upgrade it to the point that you
could lock yourself out of your own information if you have only a secret security clearance.
The following are incorrect answers because they are all FALSE:
"It allows read up" is incorrect. The "simple security" property forbids read up.
"It addresses covert channels" is incorrect. Covert channels are not addressed by the Bell-
LaPadula model.
"It addresses management of access controls" is incorrect. Management of access controls are
beyond the scope of the Bell-LaPadula model.
Reference(s) used for this question:
Hernandez CISSP, Steven (2012-12-21). Official (ISC)2 Guide to the CISSP CBK, Third Edition
((ISC)2 Press) (Kindle Locations 17595-17600). Auerbach Publications. Kindle Edition.

NEW QUESTION: 2
どのステートメントは、外側のarp 1.1.1.1 0192.7gid.0020コマンドが実行するものを記述していますか？
A. ホスト1.1.1.1のプロキシARPを設定する
B. ホスト1.1.1.1のARP検査を有効にする
C. ホスト1.1.1.1の仮想MACアドレスを割り当てます。
D. ホスト1.1.1.1の静的ARPエントリを作成します。
Answer: D
Explanation:
Explanation
That command adds a static ARP entry to allow ARP responses from the host at 1.1.1.1 with the MAC address
0009.7cbe.2100 on the outside interface
http://www.cisco.com/c/en/us/td/docs/security/asa/asa82/configuration/guide/config/fwmode.html#wp1224694

NEW QUESTION: 3
Which of the following is not a limitation of the univariate Gaussian model to capture the codependence structure between risk factros used for VaR calculations?
A. The univariate Gaussian model fails to fit to the empirical distributions of risk factors, notably their fat tails and skewness.
B. Determining the covariance matrix becomes an extremely difficult task as the number of risk factors increases.
C. A single covariance matrix is insufficient to describe the fine codependence structure among risk factors as non-linear dependencies or tail correlations are not captured.
D. It cannot capture linear relationships between risk factors.
Answer: D
Explanation:
Explanation
In the univariate Gaussian model, each risk factor is modeled separately independent of the others, and the dependence between the risk factors is captured by the covariance matrix (or its equivalent combination of the correlation matrix and the variance matrix). Risk factors could include interest rates of different tenors, different equity market levels etc.
While this is a simple enough model, it has a number of limitations.
First, it fails to fit to the empirical distributions of risk factors, notably their fat tails and skewness. Second, a single covariance matrix is insufficient to describe the fine codependence structure among risk factors as non-linear dependencies or tail correlations are not captured. Third, determining the covariance matrix becomes an extremely difficult task as the number of risk factors increases. The number of covariances increases by the square of the number of variables.
But an inability to capture linear relationships between the factors is not one of the limitations of the univariate Gaussian approach - in fact it is able to do that quite nicely with covariances.
A way to address these limitations is to consider joint distributions of the risk factors that capture the dynamic relationships between the risk factors, and that correlation is not a static number across an entire range of outcomes, but the risk factors can behave differently with each other at different intersection points.