500 word-long discussion post, identify two potential information security benefits for moving to the cloud and two potential information security drawbacks for being in the cloud. 

Compare and contrast two fundamental security design principles. Analyze how these principles and how they impact an organization’s security posture.  

Please refer below links for more info

https://nvlpubs.nist.gov/nistpubs/Legacy/SP/nistspecialpublication800-111.pdf

Review the material on routers.

It is sometimes said that information extracted from a router or switch does not necessarily provide specific evidence of a particular crime. What is meant by that?  If true, what then is the most useful information collected from these devices in an investigation?

Do the exercises below.

questions based on chapter 2 exercises:

(1) based on exercise 22

A) Describe how to map values from the interval [-1,1] to the interval [0,1].  

B) Consider two time series as two sets on numbers: e.g. S1 = {3, 5, 7, 8 , .., 4, 7} and S2 = {5, 5, 8 , .., 9, 7}. You need to come up with 2 sets, each having 100 elements. You can make them random if you like.

1) Normalize each set in range [0,1].

2) Find the probabilities of S2 in the ranges [0,0.1], (0.1,0.2], (0.2,0.3], .., (0.9,1] when S1 are in the same ranges [0,0.1], (0.1,0.2], (0.2,0.3], .., (0.9,1] respectively,  (find probability of value of S2 in the range, given the value of S1 is in the same range).

questions based on chapter 4 exercises:

(2) based on exercise 12

Let X be a binomial random variable with mean N * p and variance N * p * (1−p).

A) Show that the ratio X/N also has a binomial distribution

B) Show that the ratio X/N also has mean p

C) Show that the ratio X/N also has variance p * (1 − p) / N

questions based on chapter 5 exercises:

(3) based on exercise 6 

A) Suppose the fraction of undergraduate students who smoke is 15% and the fraction of graduate students who smoke is 23%. If one-fifth of the college students are graduate students and the rest are undergraduates, what is the probability that a student who smokes is a graduate student?

B) Given the information in part (a), is a randomly chosen college student more likely to be a graduate or undergraduate student?

C) Repeat part (b) assuming that the student is a smoker.

D) Suppose 30% of the graduate students live in a dorm but only l0% of the undergraduate students live in a dorm. If a student smokes and lives in the dorm, is he or she more likely to be a graduate or undergraduate student? You can assume independence between students who live in a dorm and those who smoke.

Chapter 5 exercises

20. Consider the task of building a classifier from random data, where the attribute values are generated randomly irrespective of the class labels. Assume the data set contains records from two classes, “+” and “−.” Half of the data set is used for training while the remaining half is used for testing.

(a) Suppose there are an equal number of positive and negative records in the data and the decision tree classifier predicts every test record to be positive. What is the expected error rate of the classifier on the test data?

(b) Repeat the previous analysis assuming that the classifier predicts each test record to be positive class with probability 0.8 and negative class with probability 0.2.

(c) Suppose two-thirds of the data belong to the positive class and the remaining one-third belong to the negative class. What is the expected error of a classifier that predicts every test record to be positive?

(d) Repeat the previous analysis assuming that the classifier predicts each test record to be positive class with probability 2/3 and negative class with probability 1/3.

Chapter 6 exercises

5. Prove Equation 6.3 in the book. (Hint: First, count the number of ways to create itemset that forms the left hand side of the rule. Next, for each size k itemset selected for the left-hand side, count the number of ways to choose the remaining d − k items to form the right-hand side of the rule.)

17. Suppose we have market basket data consisting of 100 transactions and 20 items. If the support for item a is 25%, the support for item b is 90% and the support for itemset {a, b} is 20%. Let the support and confidence thresholds be 10% and 60%, respectively.

(a) Compute the confidence of the association rule {a} -> {b}. Is the rule interesting according to the confidence measure?

(b) Compute the interest measure for the association pattern {a, b}. Describe the nature of the relationship between item a and item b in terms of the interest measure.

(c) What conclusions can you draw from the results of parts (a) and (b)?

(d) NOT NEEDED FOR THE TEST

Chapter 7 exercises

5. For the data set with the attributes given below, describe how you would convert it into a binary transaction data set appropriate for association analysis. Specifically, indicate for each attribute in the original data set.

(a) How many binary attributes it would correspond to in the transaction data set,

(b) How the values of the original attribute would be mapped to values of the binary attributes, and

(c) If there is any hierarchical structure in the data values of an attribute that could be useful for grouping the data into fewer binary attributes. The following is a list of attributes for the data set along with their possible values. Assume that all attributes are collected on a per-student basis:

• Year : Freshman, Sophomore, Junior, Senior, Graduate: Masters, Graduate: PhD, Professional

• Zip code : zip code for the home address of a U.S. student, zip code for the local address of a non-U.S. student

• College : Agriculture, Architecture, Continuing Education, Education, Liberal Arts, Engineering, Natural Sciences, Business, Law, Medical, Dentistry, Pharmacy, Nursing, Veterinary Medicine

• On Campus : 1 if the student lives on campus, 0 otherwise

• Each of the following is a separate attribute that has a value of 1 if the person speaks the language and a value of 0, otherwise.

- Arabic

- Bengali

- Chinese Mandarin

- English

- Portuguese

- Russian

- Spanish

Chapter 8 exercises

1. Consider a data set consisting of 2^(20) data vectors, where each vector has 32 components and each component is a 4-byte value. Suppose that vector quantization is used for compression and that 2^(16) prototype vectors are used. How many bytes of storage does that data set take before and after compression and what is the compression ratio?

8. Consider the mean of a cluster of objects from a binary transaction data set. What are the minimum and maximum values of the components of the mean? What is the interpretation of components of the cluster mean? Which components most accurately characterize the objects in the cluster?

9. Give an example of a data set consisting of three natural clusters, for which (almost always) K-means would likely find the correct clusters, but bisecting K-means would not.

11. Total SSE is the sum of the SSE for each separate attribute. What does it mean if the SSE for one variable is low for all clusters? Low for just one cluster? High for all clusters? High for just one cluster? How could you use the per variable SSE information to improve your clustering?

13. The Voronoi diagram for a set of 1( points in the plane is a partition of all the points of the plane into K regions, such that every point (of the plane) is assigned to the closest point among the 1( specified points. (See Figure 8.38.) What is the relationship between Voronoi diagrams and K-means clusters? What do Voronoi diagrams tell us about the possible shapes of K-means clusters?