Date: March 14, 2020

To: CISO, Padgett-Beale Inc.

From: Jyotsna Pakhare

Subject: Travel Request- National Cyber Summit: Alabama, USA.

Attached please find my Travel request to attend the National Cyber Summit, that is being held in Huntsville, Alabama. I would like to attend this Conference, which is held annually, and the dates are June 2-4, 2020. This year the conference is being held at the Braun Center in Huntsville, Alabama.

National Cyber Summit is the nation’s most innovative cyber security-technology event, that offer unique educational, collaborative and workforce development opportunities for industry visionaries and rising leaders. (NCS,2020) This Conference will be attended by government and commercial participants.

The event is hosted by the North Alabama Chapter of the Information Systems Security Association (NAC-ISSA) Cyber Huntsville Corporation (CHC), Auburn University Research, and University of Alabama in Huntsville. It is also Supported by the National Initiative for Cybersecurity Education (NICE), a program of the National Institute of Standards and Technology in the U.S. Department of Commerce, under Grant # 70NANB15H016.

The Summit will provide 4 days of educational training and have over 60 expert speakers, over 60 sessions (Advanced Manufacturing/SCADA/Supply Chain, Law Enforcement/Forensics, Redstone Arsenal Cyber Ecosystem, Research, and Technical), and over 70 cyber-focused exhibitors (including NIST, NSA, FBI, and the NICE Challenge Project). This Summit offers comprehensive, educational sessions covering critical security issues and emerging security issues for our organizations.

Attending this conference would be a great addition to my knowledge and expertise and help enhance my knowledge about Advanced Manufacturing, Social Engineering, Human Security, Cyber Skills Gap, Cyber Resilience, Forensics, Security Testing, Reverse Engineering, Information Security as Risk Management, Risk Analysis, Malware, Block chain, Policy, Cloud, and Latest Hacks which this summit will be offering. It will also give me a chance to meet experts in the field and share thoughts and ideas on the latest trends and firsthand information about actual end user experiences from participants and leaders.

The three days of dedicated time to knowledge and insight on networking, special events will give me a chance to meet leaders in the industry, share ideas, gain knowledge and increase our company profile. There will be product demos and engage with sponsors. Will also receive Continuing Professional Education Credits that will apply to maintaining current certifications (such as CISSP, CEH, Security+ CE, and many others). Given an opportunity to attend this Conference will be a great asset to the company and to my knowledge to serve the company better and utilize my knowledge to the betterment of PBI.

Costs:

The approximate cost associated with the attendance if the Conferences is as follows:

Travel Costs (Flights & Transportation to and from Airport): $250- $350

Accommodations (4 Nights at $95-$150/night): $380 – $600(Special negotiated costs for attendees)

Meals (5 days at $51): $255

Conference Pass for 3 days: $510

Total cost to attend the National Cyber Summit at the Von Braun Center in Huntsville, Alabama for 4 days and 4 nights from June 1-5 would be: $1600

Justification:

I believe attending the National Cyber Summit and the knowledge gained through the experts in this field will better help myself and PBI to know about the looming cybersecurity risks facing our company in the near future, will make us aware of the upcoming threats and latest issues in cyber crime and security. Taking precautionary steps by using this knowledge will help PBI curtail and avoid any further costs which are much more compared to this travel cost, that may result from any unfortunate cyber related event occurring in PBI. Budget for the Conference is a small price to pay for a future loss that may cost us much more in reputation and finances.

My learning from the Conference will be shared with my department and IT teams to enhance their knowledge and by creating a presentation of my learning and new knowledge will only help and enhance security at PBI. Upon my return I plan on having a meeting and presenting my learning and details about the Conference to my colleagues.

Thank you for your consideration

Jyotsna Pakhare.

References:

Cybersecurity Conferences 2020 – 2021 [Updated Hourly]. (n.d.). Retrieved March 12, 2020, from https://infosec-conferences.com/

National Cyber Summit 2020: InfoSec Conference Alabama. (2019, November 22). Retrieved March 12, 2020, from https://infosec-conferences.com/events-in-2020/nat…

The 10- to 12-page final document will consolidate the work that you have completed in the steps along with some additional elements (scope, executive summary, etc.) to create a comprehensive postmigration activity report

Step 1: Postmigration
Activities Overview

Step 2: Explore AWS
Cloud Formation

Step 3: Compare
Monitoring/Management Options in AWS

Step 4: Evaluate and
Configure AWS Trusted Advisor

Step 5: Evaluate and Configure AWS Systems Manager

Step 6: Discuss and Review Final Cutover/Rollback options

Step 7: Write Cloud Postmigration Activity Report

The 10- to 12-page final document will consolidate the work that you have completed in the steps along with some additional elements (scope, executive summary, etc.) to create a comprehensive postmigration activity report

Need lab reports done in AWS.

You will start with overviewing and discussing these activities in Step 1.

In Step 2, you will get acquainted with CloudFormation – AWS Infrastructure as Code (and as graphics) feature.

You will create a template for a web server setup. Follow the AWS guide carefully and click in proper places exactly, or your template will fail.

Step 3 is to compare monitoring and management options in AWS: Trusted Advisor and Systems Manager, and write a short report.

You will then configure these features in AWS, following AWS guides in Steps 4 and 5.

Step 6 is to review and discuss final cut-over and rollback options. In Step 7 you will write your final Postmigration Activity Report, as per the template provided.

Western civilization 2

Directions:

I will upload the articles and textbook pages

No outside information only use information from articles and textbook must have evidence to back it up from the textbook or articles I’ll tell you the textbook name for bibliography page grammer is very important no plagiarism with plagiarism report

I’ll be checking to make sure information is only from the textbook and articles must use all articles in the assignment

Mla format is fine must use citations example is below and must cite properly in essay

• Using the documents, you are to accomplish the following tasks in a paper of 4-8 pages address the following 3 objectives:

For each 19th century ideology listed (liberalism, conservatism, feminism, Marxism, socialism, capitalism):

1. Identify the major ideas the ideology espouses.

2. Use the documents provided to support the ideas identified above. For example: if you are describing the main ideas of conservatism, you should read the documents about conservatism and quote from them to support the idea you are writing about.

3. Next you must connect the ideology to history. So answer the following question: Historically, how did the ideologies influence Western Civilization? (You can focus on the 19th century unless you know a bit about the last century). For example, clearly Marxism resulted from the Industrial Revolution. Explain how.

4. Decide which ideology you favor and explain why.

5. You must pull information from the documents. Quote directly and often.

Searching for alternative sources is frankly unnecessary. You may use the textbook to unnecessary. You may use the textbook to supplement your paper, but you must make copious use of the documents. This assessment requires students to read and use the words of the thinkers of the 19th century. Pure generalization without necessary evidence will result in a failing grade.

Standard 12-point font and double spaced. A bibliography is required but you need only list the documents that you utilized in the paper. Again, this should be obvious, as you will need to quote often from them to buttress your argument. Use internal citations when quoting a document. Example: Marx intimated that 19th century society was ready for revolutionary change when he stated “the history of all hitherto existing society is the history of class struggle” (Marx p.1)

Natural Language Processing

Q1 Review the python script in Q1 Folder – NLTK_Text_Analysis.py

Use text below to apply the same process

Text= “””Backgammon is one of the oldest known board games. Its history can be traced back nearly 5,000 years to archeological discoveries in the Middle East. It is a two player game where each player has fifteen checkers which move between twenty-four points according to the roll of two dice.”””

a. Text Analysis Operations using NLTK

b. Tokenization

c. Stopwords removal

d. Lexicon Normalization such as Stemming and Lemmatization

e. POS Tagging

Q2 Analyze the customer reviews in the file Restaurant_Reviews.tsv

Explain each step for the following text clean-up commands

a. Explain each step for the following text clean-up commands

review = dataset[‘Review’][0]

review = re.sub(‘[^a-zA-Z]’, ‘ ‘, dataset[‘Review’][0])

review = review.lower()

review = review.split()

ps = PorterStemmer()

review = [ps.stem(word) for word in review if not word in set(stopwords.words(‘english’))]

review = ‘ ‘.join(review)

b. What is the classification question?

c. The example uses the Naïve Bayes classifier to classify the sentiments. Calculate the confusion matrix:

TP = # True Positives,

TN = # True Negatives,

FP = # False Positives,

FN = # False Negatives):

Accuracy = (TP + TN) / (TP + TN + FP + FN)

d. Apply the logistic regression classifier to the problem – recalculate “c” i.e. TP, TN, FP, FN, Accuracy

Q3 NLTK Corpus on Movie Reviews

Q3a Use the following reference analyze sentiment analysis on Movie Review “Q3 Movie Reviews.py”

https://www.nltk.org/book/ch06.html

Q3b – Explain how the Bag of Words model help in sentiment analysis

http://blog.chapagain.com.np/python-nltk-sentiment…

Summarize the entire code in NLTKMovieReview.py file as a part of the solution

Q4 Twitter Analysis sentiment140

Perform a Twitter sentiment analysis –

Users on twitter create short messages called tweets to be shared with other twitter users

– who interact by retweeting and responding?

– Twitter employs a message size restriction of 280 characters or less

– forces the users to stay focused on the message they wish to disseminate.

– Twitter data is great for Machine Learning (ML) task of sentiment analysis.

– Sentiment Analysis falls under Natural Language Processing (NLP)

The training data is obtained from Sentiment140

– made up of about 1.6 million random tweets

– with corresponding binary labels. 0 for Negative sentiment and 1 for Positive sentiment.

Use Naive Bayes Classifier to learn the correct labels from this training set.

https://towardsdatascience.com/the-real-world-as-s…

Q5 Analyze Clothing Reviews

https://www.kaggle.com/nicapotato/womens-ecommerce…

A women’s Clothing E-Commerce site revolving around the reviews written by customers. This dataset includes 23486 rows and 10 feature variables. Each row corresponds to a customer review, and includes the variables:

Clothing ID: Integer Categorical variable that refers to the specific piece being reviewed.

Age: Positive Integer variable of the reviewers age.

Title: String variable for the title of the review.

Review Text: String variable for the review body.

Rating: Integer variable for the product score granted by the customer from 1 Worst, to 5 Best.

Recommended IND: Binary variable stating where the customer recommends the product where 1 is recommended, 0 is not recommended.

Positive Feedback Count: Positive Integer documenting the number of other customers who found this review positive.

Division Name: Categorical name of the product high level division.

Department Name: Categorical name of the product department name.

Class Name: Categorical name of the product class name

Perform

a. Text extraction & creating a corpus

b. Text Pre-processing

c. Create the DTM & TDM from the corpus

d. Exploratory text analysis

e. Feature extraction by removing sparsity

f. Build the Classification Models and compare Logistic Regression to Random Forest regression

https://medium.com/analytics-vidhya/customer-revie…

Q6 Sentiment analysis on Trump and Hillary tweets (Optional)

https://www.kaggle.com/pavanraj159/sentiment-analy…

a. Import Data and Data manipulation

b. Percentage of retweets

c. Languages used in tweets

d. Original authors of retweets

e. tweets by month

f. import positive and negative words dictionaries

g. Scoring tweets & distribution

h. Sentiment distribution of tweets and correlation matrix

i. Popular hashtags & wordcloud – hashtags

j. Popular twitter account reference & wordcloud

k. popular words in tweets, word cloud – popular words

l. Popular positive and negative words used by trump

m. Hashtag references by twitter accounts & Account references by hashtag

n. Positive & Negative word references by Trump

o. Sentiment of tweets by hour of day

p. Favorite and retweets by sentiment, Average retweets and favorites by sentiment

Q7 Sentiment Analysis of Movie Reviews – Dataset available on Kaggle (Optional)

https://www.kaggle.com/c/sentiment-analysis-on-mov…

Dataset has four columns PhraseId, SentenceId, Phrase, and Sentiment. This data has 5 sentiment labels: 0 – negative 1 – somewhat negative 2 – neutral 3 – somewhat positive 4 – positive

Perform Naïve Bayes Classification using scikit-learn.

Q8 Analyze state of the Union Address (Optional)

https://www.kaggle.com/rtatman/state-of-the-union-…

The State of the Union is an annual address by the President of the United States before a joint session of congress. In it, the President reviews the previous year and lays out his legislative agenda for the coming year This dataset contains the full text of the State of the Union address from 1989 (Regan) to 2017 (Trump).

a. Topic modelling: Which topics have become more popular over time? Which have become less popular?

b. Sentiment analysis: Are there differences in tone between different Presidents? Presidents from different parties?

Q1 Review the python script in Q1 Folder – NLTK_Text_Analysis.py

Use text below to apply the same process

Text= “””Backgammon is one of the oldest known board games. Its history can be traced back nearly 5,000 years to archeological discoveries in the Middle East. It is a two player game where each player has fifteen checkers which move between twenty-four points according to the roll of two dice.”””

a. Text Analysis Operations using NLTK

b. Tokenization

c. Stopwords removal

d. Lexicon Normalization such as Stemming and Lemmatization

e. POS Tagging

Q2 Analyze the customer reviews in the file Restaurant_Reviews.tsv

Explain each step for the following text clean-up commands

a. Explain each step for the following text clean-up commands

review = dataset[‘Review’][0]

review = re.sub(‘[^a-zA-Z]’, ‘ ‘, dataset[‘Review’][0])

review = review.lower()

review = review.split()

ps = PorterStemmer()

review = [ps.stem(word) for word in review if not word in set(stopwords.words(‘english’))]

review = ‘ ‘.join(review)

b. What is the classification question?

c. The example uses the Naïve Bayes classifier to classify the sentiments. Calculate the confusion matrix:

TP = # True Positives,

TN = # True Negatives,

FP = # False Positives,

FN = # False Negatives):

Accuracy = (TP + TN) / (TP + TN + FP + FN)

d. Apply the logistic regression classifier to the problem – recalculate “c” i.e. TP, TN, FP, FN, Accuracy

Q3 NLTK Corpus on Movie Reviews

Q3a Use the following reference analyze sentiment analysis on Movie Review “Q3 Movie Reviews.py”

https://www.nltk.org/book/ch06.html

Q3b – Explain how the Bag of Words model help in sentiment analysis

http://blog.chapagain.com.np/python-nltk-sentiment…

Summarize the entire code in NLTKMovieReview.py file as a part of the solution

Q4 Twitter Analysis sentiment140

Perform a Twitter sentiment analysis –

Users on twitter create short messages called tweets to be shared with other twitter users

– who interact by retweeting and responding?

– Twitter employs a message size restriction of 280 characters or less

– forces the users to stay focused on the message they wish to disseminate.

– Twitter data is great for Machine Learning (ML) task of sentiment analysis.

– Sentiment Analysis falls under Natural Language Processing (NLP)

The training data is obtained from Sentiment140

– made up of about 1.6 million random tweets

– with corresponding binary labels. 0 for Negative sentiment and 1 for Positive sentiment.

Use Naive Bayes Classifier to learn the correct labels from this training set.

https://towardsdatascience.com/the-real-world-as-s…

Q5 Analyze Clothing Reviews

https://www.kaggle.com/nicapotato/womens-ecommerce…

A women’s Clothing E-Commerce site revolving around the reviews written by customers. This dataset includes 23486 rows and 10 feature variables. Each row corresponds to a customer review, and includes the variables:

Clothing ID: Integer Categorical variable that refers to the specific piece being reviewed.

Age: Positive Integer variable of the reviewers age.

Title: String variable for the title of the review.

Review Text: String variable for the review body.

Rating: Integer variable for the product score granted by the customer from 1 Worst, to 5 Best.

Recommended IND: Binary variable stating where the customer recommends the product where 1 is recommended, 0 is not recommended.

Positive Feedback Count: Positive Integer documenting the number of other customers who found this review positive.

Division Name: Categorical name of the product high level division.

Department Name: Categorical name of the product department name.

Class Name: Categorical name of the product class name

Perform

a. Text extraction & creating a corpus

b. Text Pre-processing

c. Create the DTM & TDM from the corpus

d. Exploratory text analysis

e. Feature extraction by removing sparsity

f. Build the Classification Models and compare Logistic Regression to Random Forest regression

https://medium.com/analytics-vidhya/customer-revie…

Q6 Sentiment analysis on Trump and Hillary tweets (Optional)

https://www.kaggle.com/pavanraj159/sentiment-analy…

a. Import Data and Data manipulation

b. Percentage of retweets

c. Languages used in tweets

d. Original authors of retweets

e. tweets by month

f. import positive and negative words dictionaries

g. Scoring tweets & distribution

h. Sentiment distribution of tweets and correlation matrix

i. Popular hashtags & wordcloud – hashtags

j. Popular twitter account reference & wordcloud

k. popular words in tweets, word cloud – popular words

l. Popular positive and negative words used by trump

m. Hashtag references by twitter accounts & Account references by hashtag

n. Positive & Negative word references by Trump

o. Sentiment of tweets by hour of day

p. Favorite and retweets by sentiment, Average retweets and favorites by sentiment

Q7 Sentiment Analysis of Movie Reviews – Dataset available on Kaggle (Optional)

https://www.kaggle.com/c/sentiment-analysis-on-mov…

Dataset has four columns PhraseId, SentenceId, Phrase, and Sentiment. This data has 5 sentiment labels: 0 – negative 1 – somewhat negative 2 – neutral 3 – somewhat positive 4 – positive

Perform Naïve Bayes Classification using scikit-learn.

Q8 Analyze state of the Union Address (Optional)

https://www.kaggle.com/rtatman/state-of-the-union-…

The State of the Union is an annual address by the President of the United States before a joint session of congress. In it, the President reviews the previous year and lays out his legislative agenda for the coming year This dataset contains the full text of the State of the Union address from 1989 (Regan) to 2017 (Trump).

a. Topic modelling: Which topics have become more popular over time? Which have become less popular?

b. Sentiment analysis: Are there differences in tone between different Presidents? Presidents from different parties?

HW11.docx

Q2 Restaurant Reviews.zip

Q1 NLP Basics.zip

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