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?
300 WORDS
Part A
Create 1- to 2-page summary, and do the following:
Submit your assignment.
Write a C or C++ program to run on a Unix platform. This program will take three arguments. The first argument will be the pathname of a directory on the system. The second argument will be a character string. The third argument is the maximum number of output lines, N. Your program should display at most N entries in the directory tree in a text file.
You need to implement three system calls: Open, Read, and Stat
Details of the system calls are given below. You can also use man command pages to learn more about these system calls.
1. Open system call: DIR *opendir (const char *dirname) Opendir system call opens a directory and returns a pointer to a struct DIR. DIR represents a directory stream, which is an ordered sequence of all directory entries in a particular directory.
2. Read system call: struct dirent *readdir (DIR *dirp) Once the directory has been opened, you can read the directory with readdir system call. Each time you call readdir system call, it returns another entry in the directory.
3. Stat system call: int stat (const char *path, struct stat *sb) Once you have the name of an entry, you can use the stat system call to get more information about it. stat function takes two arguments, the first is a path name and second is a pointer to struct stat, which contains information about the file.
Guidelines and Requirements
1. You can use either C or C++ for this programming assignment.
2. Your program should run on a UNIX platform
3. Add comments (about the function/variable/class) to your code as much as possible
4. Zip your project including source file(s) and input text data files (if any)
According to Kirk (2016), The essence of “Formulating Your Brief” is to “identify the context in which your work will be undertaken and then define its aims: it is the who, what, where, when and how.” It could be formal or informal as any project you think you must make it. This phase is where you create a vision for your work.
1. Why is it so important to formulate your brief for a data presentation? Discuss some ways you would implement to formulate an effective brief. What are some advantages to your methods? What are some disadvantages?
2. Please provide a summary as to whether you agree/disagree with their advantages/disadvantages.
– apa
– 2pages
– no plagiarism
– 2 references
Reference
Kirk, A. (2016). Data Visualisation: A Handbook for Data Driven Design. Thousand Oaks, CA: Sage Publications, Ltd.
Scenario
Your new manager comes to you and asks you that he keeps hearing about read/write blockers for forensic imaging. He’s not sure what that is. He also is confused because he’s heard that there’s two different types (software and hardware). Also, there’s commercial and open source tools. He knows you just took a course in digital forensics so he asks you to prepare a memo for him explaining all that.
Your assignment
What this teaches you
Being familiar with what tools the investigator used will help you. You gain credibility by asking what hardware or software tools they used, how they deployed it and why they went with a hardware or software version.
1. Briefly explain any steps you are taking, or plan to take, to gain hands-on experience in your program of study(Ph.D. in information technology). 100 words use own words
2. State two goals you hope to achieve through applying your coursework( Data Science & Big Data Analy, information security leadership & communication) this term to your workplace experience. 100 words (use own words)
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?
Instructions
In order to complete assignment #5, you will need to answer the below questions. Please complete the questions in a Word document and then upload the assignment for grading. When assigning a name to your document please use the following format (last name_Assignment #5). Use examples from the readings, lecture notes, and outside research to support your answers. The assignment must be a minimum of a 1-full page in length with a minimum of 2 – outside sources. Please be sure to follow APA guidelines for citing and referencing sources. Assignments are due by 11:59 pm Eastern time on Sunday.
Chapter 8
Review Question #4 on page 261 and then answer the following question:
The new age of technology presents many opportunities for litigation. The Internet is no exception. When operating Internet websites, an important part of it is owning the domain name (www.example.com). Anyone in the world can own any domain name that is available and the facts of this case arise from this concept.
The plaintiff, in this case, Weather Underground Corporation (Weather Underground), a Michigan corporation, is a commercial weather service. It owns and operates several domain names so that people can access their company through their websites. Defendants, in this case, Navigation Catalyst Systems, Incorporated (“NCS”), a Delaware corporation, owns many domain names that are similar to the plaintiff’s company name (some would result from people misspelling the correct domain name for Weather Underground). NCS profits from consumers going to one of these websites and clicking on links that are on them.
Plaintiff filed suit against NCS and several of its companies in the District Court for the Eastern District of Michigan. As defendants were not incorporated in Michigan, the issue of personal jurisdiction arise. The courts of appeals have held that in order to establish specific personal jurisdiction (showing that this company has established contacts with the forum state), one must show three things: (1) the defendant purposefully availed himself of the privilege of acting in the forum state, (2) the cause of action arises from the defendant’s activities there, and (3) the defendant’s acts were so substantial as to make the exercise of personal jurisdiction there reasonable.
The district court is considering whether the exercise of personal jurisdiction is proper. What should it decide and why?
Weather Underground Inc. v. Navigation Catalyst Sys. Inc., No. 09–10756, 2009 WL 3818191 (E.D. Mich. Nov. 13, 2009).
The district court is considering whether the exercise of personal jurisdiction is proper. What should it decide and why?
Chapter 9
Apple, Inc., and Major League Baseball (MLB) signed an agreement for the broadcast of games. MLB will offer two live games per day, subject to blackout restrictions. Then MLB plans to roll out an entire offering of out-of-market games currently offered only through its premium live streaming video service. Identify some other, extra features users want. Identify restrictions that MLB will want to see in the agreement.
Mitigation of DDoS Attacks in Cloud Computing research PPT
1. what tools you are using,
whawhat approach you are using and what are you investigating ?