Category: Web

– Look at the lab 1 and lab 2 to setup cybersecurity lab in virtual box for assignment.
– Download virtual machines(ubuntu mate 22.04) , owasp,metasploitable) with the vm specifications provided in lab 1 and lab 2 to conduct testing on the target machine(wetransfer link attached)
-Use advanced penetration tools like Nmap, etherape, wireshark and more.
-Document every single step in penetration testing and write a 2500 word report that includes screenshots and text. Use name ‘Name’ then vm name for screenshots so that it can show in terminal commands and so that it can be shown with my name.

– Look at the lab 1 and lab 2 to setup cybersecurity lab in virtual box for assignment.
– Download virtual machines(ubuntu mate 22.04) , owasp,metasploitable) with the vm specifications provided in lab 1 and lab 2 to conduct testing on the target machine(wetransfer link attached)
-Use advanced penetration tools like Nmap, etherape, wireshark and more.
-Document every single step in penetration testing and write a 2500 word report that includes screenshots and text. Use name ‘Name’ then vm name for screenshots so that it can show in terminal commands and so that it can be shown with my name.

One of the main objectives of this course is to help you gain hands-on experience in communicating insightful and impactful findings to stakeholders. In this project you will use the tools and techniques you learned throughout this course to train a few classification models on a data set that you feel passionate about, select the regression that best suits your needs, and communicate insights you found from your modeling exercise.
Step by Step Assignment Instructions
Setup instructions:
Before you begin, you will need to choose a data set that you feel passionate about. You can brainstorm with your peers about great public data sets using the discussion board in this module.
Please also make sure that you can print your report into a pdf file.
How to submit:
The format of your work must adhere to the following guidelines. The report should be submitted as a pdf. Optionally, you can include a python notebook with code.
Make sure to include mainly insights and findings on your report. There is no need to include code, unless you want to.
Project
Optional: find your own data set
As a suggested first step, spend some time finding a data set that you are really passionate about. This can be a data set similar to the data you have available at work or data you have always wanted to analyze. For some people this will be sports data sets, while some other folks prefer to focus on data from a datathon or data for good.
Optional: participate in a discussion board
As an optional step, go into a discussion board and brainstorm with your peers great data sets to analyze. If you prefer to skip this step, feel free to use the Ames housing data set or the Churn phone data set that we used throughout the course.
Required
Once you have selected a data set, you will produce the deliverables listed below and submit them to one of your peers for review. Treat this exercise as an opportunity to produce analysis that are ready to highlight your analytical skills for a senior audience, for example, the Chief Data Officer, or the Head of Analytics at your company.
Sections required in your report:
Main objective of the analysis that specifies whether your model will be focused on prediction or interpretation and the benefits that your analysis provides to the business or stakeholders of this data.
Brief description of the data set you chose, a summary of its attributes, and an outline of what you are trying to accomplish with this analysis.
Brief summary of data exploration and actions taken for data cleaning and feature engineering.
Summary of training at least three different classifier models, preferably of different nature in explainability and predictability. For example, you can start with a simple logistic regression as a baseline, adding other models or ensemble models. Preferably, all your models use the same training and test splits, or the same cross-validation method.
A paragraph explaining which of your classifier models you recommend as a final model that best fits your needs in terms of accuracy and explainability.
Summary Key Findings and Insights, which walks your reader through the main drivers of your model and insights from your data derived from your classifier model.
Suggestions for next steps in analyzing this data, which may include suggesting revisiting this model after adding specific data features that may help you achieve a better explanation or a better prediction.
After going through some guided steps, you will have insights that either explain or predict your outcome variable. As a main deliverable, you will submit a report that helps you focus on highlighting your analytical skills and thought process.
Grading Criteria Overview
Your peer will review your report from the perspective of a Chief Data Officer or the Head of Analytics and will assess whether your final linear regression went through all the necessary steps to achieve the main objective of your analysis.
Yes, you are expected to leverage a wide variety of tools, but this report should focus on presenting findings, insights, and next steps. You may include some visuals from your code output, but this report is intended as a summary of your findings, not a code review. Optionally, you can submit your code as a python notebook or as a print out in the appendix of your document.
The grading will center around 5 main points:
Does the report include a section describing the data?
Does the report include a paragraph detailing the main objective(s) of this analysis?
Does the report include a section with variations of classifier models and specifies which one is the model that best suits the main objective(s) of this analysis?
Does the report include a clear and well presented section with key findings related to the main objective(s) of the analysis?
Does the report highlight possible flaws in the model and a plan of action to revisit this analysis with additional data or different predictive modeling techniques?
Frequently Asked Questions
Here are frequently asked questions about the assignment and review process. Please read these before starting your assignment.
Do I have to come up with my own data set?
You are highly encouraged to find a data set you feel really passionate about. This will help you showcase analytical work that truly matches your skills. But if you prefer, you can use some of the data sets from this course.
Is it OK to choose the same data set as someone else?
Yes, more than one person can analyze the same data set. Most likely your insights will be different from your peers and you will still be able to showcase your own talent as a unique solution.
Do I have to train more than 3 different classifiers?
It is highly recommended that you try at least three different classifiers to highlight which tool or technique improved your prediction or interpretation.
Is this an individual assignment?
You can ask for help or assistance on technical issues and general direction of your analysis, but the interpretation of the analytical output and the writing of the report should be your own.
Comments from Customer
Discipline: AI

One of the main objectives of this course is to help you gain hands-on experience in communicating insightful and impactful findings to stakeholders. In this project you will use the tools and techniques you learned throughout this course to train a few classification models on a data set that you feel passionate about, select the regression that best suits your needs, and communicate insights you found from your modeling exercise.
Step by Step Assignment Instructions
Setup instructions:
Before you begin, you will need to choose a data set that you feel passionate about. You can brainstorm with your peers about great public data sets using the discussion board in this module.
Please also make sure that you can print your report into a pdf file.
How to submit:
The format of your work must adhere to the following guidelines. The report should be submitted as a pdf. Optionally, you can include a python notebook with code.
Make sure to include mainly insights and findings on your report. There is no need to include code, unless you want to.
Project
Optional: find your own data set
As a suggested first step, spend some time finding a data set that you are really passionate about. This can be a data set similar to the data you have available at work or data you have always wanted to analyze. For some people this will be sports data sets, while some other folks prefer to focus on data from a datathon or data for good.
Optional: participate in a discussion board
As an optional step, go into a discussion board and brainstorm with your peers great data sets to analyze. If you prefer to skip this step, feel free to use the Ames housing data set or the Churn phone data set that we used throughout the course.
Required
Once you have selected a data set, you will produce the deliverables listed below and submit them to one of your peers for review. Treat this exercise as an opportunity to produce analysis that are ready to highlight your analytical skills for a senior audience, for example, the Chief Data Officer, or the Head of Analytics at your company.
Sections required in your report:
Main objective of the analysis that specifies whether your model will be focused on prediction or interpretation and the benefits that your analysis provides to the business or stakeholders of this data.
Brief description of the data set you chose, a summary of its attributes, and an outline of what you are trying to accomplish with this analysis.
Brief summary of data exploration and actions taken for data cleaning and feature engineering.
Summary of training at least three different classifier models, preferably of different nature in explainability and predictability. For example, you can start with a simple logistic regression as a baseline, adding other models or ensemble models. Preferably, all your models use the same training and test splits, or the same cross-validation method.
A paragraph explaining which of your classifier models you recommend as a final model that best fits your needs in terms of accuracy and explainability.
Summary Key Findings and Insights, which walks your reader through the main drivers of your model and insights from your data derived from your classifier model.
Suggestions for next steps in analyzing this data, which may include suggesting revisiting this model after adding specific data features that may help you achieve a better explanation or a better prediction.
After going through some guided steps, you will have insights that either explain or predict your outcome variable. As a main deliverable, you will submit a report that helps you focus on highlighting your analytical skills and thought process.
Grading Criteria Overview
Your peer will review your report from the perspective of a Chief Data Officer or the Head of Analytics and will assess whether your final linear regression went through all the necessary steps to achieve the main objective of your analysis.
Yes, you are expected to leverage a wide variety of tools, but this report should focus on presenting findings, insights, and next steps. You may include some visuals from your code output, but this report is intended as a summary of your findings, not a code review. Optionally, you can submit your code as a python notebook or as a print out in the appendix of your document.
The grading will center around 5 main points:
Does the report include a section describing the data?
Does the report include a paragraph detailing the main objective(s) of this analysis?
Does the report include a section with variations of classifier models and specifies which one is the model that best suits the main objective(s) of this analysis?
Does the report include a clear and well presented section with key findings related to the main objective(s) of the analysis?
Does the report highlight possible flaws in the model and a plan of action to revisit this analysis with additional data or different predictive modeling techniques?
Frequently Asked Questions
Here are frequently asked questions about the assignment and review process. Please read these before starting your assignment.
Do I have to come up with my own data set?
You are highly encouraged to find a data set you feel really passionate about. This will help you showcase analytical work that truly matches your skills. But if you prefer, you can use some of the data sets from this course.
Is it OK to choose the same data set as someone else?
Yes, more than one person can analyze the same data set. Most likely your insights will be different from your peers and you will still be able to showcase your own talent as a unique solution.
Do I have to train more than 3 different classifiers?
It is highly recommended that you try at least three different classifiers to highlight which tool or technique improved your prediction or interpretation.
Is this an individual assignment?
You can ask for help or assistance on technical issues and general direction of your analysis, but the interpretation of the analytical output and the writing of the report should be your own.
Comments from Customer
Discipline: AI

Before beginning work on this interactive assignment, review Chapter 10 in the textbook, and review any relevant information in this week’s lecture. Access the MISM Credible Resource Guide for assistance with finding additional sources and information on this topic. For this interactive assignment access your virtual lab environment and follow the instructions provided within the lab.
As a network administrator of a company, you are faced with many networking problems that occur on a daily basis. Having an arsenal of tools and the knowledge to use them as part of your troubleshooting effort is critical. The following are common network troubleshooting commands. Be aware that many of these have useful switches that expand the command’s capabilities.
IPConfig
Tracert
Ping
Nslookup
GetMac
Netstat
Tasklist
Taskkill
Netsh
Netuse
Dnslookup
Arp
Route
Shutdown
Within your virtual lab environment you will follow the instructions provided. In the virtual lab, create a PDF with a screenshot of each complete step of the instructions. (Be sure to include two screenshots per page in your PDF.) Once you have completed the exercise within the virtual lab, download the PDF and attach it to your initial post.
In the body of your initial post, provide an explanation of each command. Using what you completed in the virtual lab, provide examples or use scenarios that demonstrate where or how each of the commands can be used when troubleshooting network problems.
the software. If the e-mail is heading out onto the Internet, the transport layeradds a TCP header to the front of the e-mail message. The information in thisheader is used by the TCP layer at the receiving workstation to perform one ormore of the six transport functions. The TCP header contains the fields shownin Figure 10-7. Let us examine only those fields that assist TCP in performingthe six functions listed earlier.Figure10-7The fields of theTCP headerSource PortDestination PortSequence Number16 bits16 bits32 bitsAcknowledgment NumberHlenFlagsWindow32 bits4 bits16 bitsChecksumUrgent PointerOptionalVariable LengthData . . . . . . . . . .Variable LengthReserved6 bits6 bitsOptionsPadding16 bits16 bitsThe first two TCP header fields, Source Port and Destination Port, containthe addresses of the application programs at the two ends of the transport connection. These port addresses are used in creating and terminating connections.The port number can also be used to multiplex multiple transport connection sover a single IP connection. It is important to note the difference between an IP address and a port number. The IP address identifies a device connected to the Internet, while the port number identifies an application on that device. Working together, the two create what is called a socket—a precise identification of a particular application on a particular device. What if your company has one server that handles both e-mail and FTP connections? The server would have one IP address but two different port numbers: one for the e-mail application and one for the FTP application. Now let us add the fact that this server is more than likely on a local area network, and thus has a network interface card (NIC) with a unique 48-bit NIC address. Now we have three addresses. The NIC address is used only on the local area network to find a particular device. The IP address is used to move the data packet through the Internet. The port number is used to identify the particular application on a device. The Sequence Number field contains a 32-bit value that counts bytes and indicates a packet’s data position within the connection. For example, if you are in the middle of a long connection in which thousands of bytes are being transferred, the Sequence Number tells you the exact position of this packet within that sequence. This field can be used to reassemble the pieces at the receiving workstation and determine if any packets of data are missing. The Window field contains a sliding window value that provides flow control between the two endpoints. If one end of the connection wants the other end of the connection to stop sending data, the Window field can be set to 0. The Checksum field is the next, and it provides an arithmetic check-sum of the header and the data field that follows the header. The Urgent Pointer is used to inform the receiving workstation that this packet of data contains urgent data. Like its counterpart IP, TCP is a fairly streamlined protocol. Its primary goal is to create an error-free, end-to-end connection across one or more networks. TCP and IP do have their shortcomings, however, and these have The Internet281Copyright 2016 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Du
chapter10
the software. If the e-mail is heading out onto the Internet, the transport layeradds a TCP header to the front of the e-mail message. The information in thisheader is used by the TCP layer at the receiving workstation to perform one ormore of the six transport functions. The TCP header contains the fields shownin Figure 10-7. Let us examine only those fields that assist TCP in performingthe six functions listed earlier.Figure10-7The fields of theTCP headerSource PortDestination PortSequence Number16 bits16 bits32 bitsAcknowledgment NumberHlenFlagsWindow32 bits4 bits16 bitsChecksumUrgent PointerOptionalVariable LengthData . . . . . . . . . .Variable LengthReserved6 bits6 bitsOptionsPadding16 bits16 bitsThe first two TCP header fields, Source Port and Destination Port, containthe addresses of the application programs at the two ends of the transport con-nection. These port addresses are used in creating and terminating connections.The port number can also be used to multiplex multiple transport connectionsover a single IP connection.It is important to note the difference between an IP address and a port num-ber. The IP address identifies a device connected to the Internet, while the portnumber identifies an application on that device. Working together, the two cre-ate what is called asocket—a precise identification of a particular applicationon a particular device. What if your company has one server that handles bothe-mail and FTP connections? The server would have one IP address but two dif-ferent port numbers: one for the e-mail application and one for the FTP applica-tion. Now let us add the fact that this server is more than likely on a local areanetwork, and thus has a network interface card (NIC) with a unique 48-bit NICaddress. Now we have three addresses. The NIC address is used only on thelocal area network to find a particular device. The IP address is used to movethe data packet through the Internet. The port number is used to identify theparticular application on a device.The Sequence Number field contains a 32-bit value that counts bytes andindicates a packet’s data position within the connection. For example, if youare in the middle of a long connection in which thousands of bytes are beingtransferred, the Sequence Number tells you the exact position of this packetwithin that sequence. This field can be used to reassemble the pieces at thereceiving workstation and determine if any packets of data are missing.The Window field contains a sliding window value that provides flow con-trol between the two endpoints. If one end of the connection wants the otherend of the connection to stop sending data, the Window field can be set to 0.The Checksum field is the next field, and it provides for an arithmetic check-sum of the headerandthe data field that follows the header. The UrgentPointer is used to inform the receiving workstation that this packet of datacontains urgent data.Like its counterpart IP, TCP is a fairly streamlined protocol. Its primarygoal is to create an error-free, end-to-end connection across one or morenetworks. TCP and IP do have their shortcomings, however, and these haveThe Internet281Copyright 2016 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Du

The purpose of this assignment is to give you the opportunity to develop and practice the
type of 21st century skills that are critically important for IT professionals in the workplace.
These skills include critical thinking, problem solving, communication, teamwork, and
lifelong learning.
Comments from Customer
Discipline: IT in Global/Local Cultures

This assignment is to demonstrate your capability to write Python Program by calculating
Grading for the class.
Hi, Class,
This is our Term Project – Signature Assignment
Data file is in Class Content Area
This assignment is to ask you to write a Python program conduct Grading for the class -1.
Input Read the input score data from an external file given by Dr. Liu
This data file is arranged as following format (colon as delimeter)
Name. : ID : (Test1-Score) : (Test2- Score) : (HW- Score) : (Project-Score)
The percentage for semester is as following:
Test1 – 20%
Test2 – 20%
HW – 40%
Project – 20%
Processing: Computation
The total semester score = 20% * Test1 + 20% * Test2 + 40% * HW + 20% * Project
Once the total semester score is computed, use this number to determine the
Semester Grade by the following Rubric:
Total_Score > = 90. The Grade will be A
90 > Total_Score > = 80. The Grade will be B
80> Total_Score > = 70. The Grade will be C
70> Total_Score > = 60. The Grade will be D
60> Total_Score The Grade will be F
Output:
After all these steps, your program will print out the output into an External File – the Format will be like the following:
For example:
Name. ID. (Test1) (Test2) (HW) (Project) TotalScore Grade
Peter Pan. 01 80 80 80 80 80 B
Then repeat for another student until all data in the given data file is done.
Comments from Customer
Discipline: Programming of All

I need to write about Cognitive Walkthroughs evaluation methods in HCI as it is one of the analytical inspection method of usability . 1 paragraph as introduction, 1 paragraph as background, and 5 literature review and specify the gaps and then steps of how to apply it to evaluate software system in general. I will attach one publication to clarify my need
Comments from Customer
Discipline: Software Engineering

I need to write about Cognitive Walkthroughs evaluation methods in HCI as it is one of the analytical inspection method of usability . 1 paragraph as introduction, 1 paragraph as background, and 5 literature review and specify the gaps and then steps of how to apply it to evaluate software system in general. I will attach one publication to clarify my need
Comments from Customer
Discipline: Software Engineering

1. Download the LinkedIn Worksheet Download LinkedIn Worksheet.
2. Save file with the following naming protocol: LinkedIn_Worksheet_YourName.docx.
3. Complete all sections with as much detail and personal reflection as possible. This is meant to be a tool and resource to help you conduct a meaningful and effective job search. The more effort you put in, the more you can clarify your goals and expectations.
I also attach mt resume below.