代做DTS303TC、代写Python设计程序

日期：2023-10-18 10:23

XJTLU Entrepreneur College (Taicang) Cover Sheet

Module code and Title DTS303TC Big Data Security and Analytics

School Title School of AI and Advanced Computing

Assignment Title Assessment 2 – Project

Submission Deadline Wednesday, November 1st 23:59,2023

(China Time, GMT + 8)

Final Word Count N/A

If you agree to let the university use your work anonymously for teaching

and learning purposes, please type “yes” here.

I certify that I have read and understood the University’s Policy for dealing with Plagiarism,

Collusion and the Fabrication of Data (available on Learning Mall Online). With reference to this

policy I certify that:

? My work does not contain any instances of plagiarism and/or collusion.

My work does not contain any fabricated data.

By uploading my assignment onto Learning Mall Online, I formally declare

that all of the above information is true to the best of my knowledge and

belief.

Scoring – For Tutor Use

Student ID

Stage of

Marking

Marker

Code

Learning Outcomes Achieved （F/P/M/D）

(please modify as appropriate)

Final

Score

A B C

1st Marker – red

pen

Moderation

– green pen

IM

Initials

The original mark has been accepted by the moderator

(please circle as appropriate):

Y / N

Data entry and score calculation have been checked by

another tutor (please circle):

Y

2nd Marker if

needed – green

pen

For Academic Office Use Possible Academic Infringement (please tick as appropriate)

Date

Received

Days

late

Late

Penalty

? Category A

Total Academic Infringement Penalty

(A,B, C, D, E, Please modify where

necessary) _____________________

? Category B

? Category C

? Category D

? Category E

DTS303TC Big Data Security and Analytics

Coursework 2 – Project

Submission deadline: 23:59, November 1st, 2023

Percentage in final mark: 60%

Learning outcomes assessed: C, D

Individual/Group: Individual

Length: Individual Report 2000 words (+/- 10%) + Application with Source Code and

Recorded Individual Presentation (not more than 5 minutes). The length of your report must

not be longer than 15 pages. The assessment has a total of 100 marks (20 marks for Part I and

80 marks for Part II)

Late policy: 5% of the total marks available for the assessment shall be deducted from the

assessment mark for each working day after the submission date, up to a maximum of five

working days

Risks:

? Please read the coursework instructions and requirements carefully. Not following these

instructions and requirements may result in loss of marks.

? The formal procedure for submitting coursework at XJTLU is strictly followed. Submission

link on Learning Mall will be provided in due course. The submission timestamp on Learning

Mall will be used to check late submission.

__________________________________________________________________

PART I: Data Cryptography and Access Control (20%)

Cryptography includes a set of techniques for scrambling or disguising data so that it is available

only to someone who can restore the data to its original form. In current computer systems,

cryptography provides a strong, economical basis for keeping data secret and for verifying data

integrity. Please answer the following questions:

Question 1: (5 marks)

Perform some research and discuss the cryptosystems and encryption schemes used to secure

the following applications.

(i) Privacy Enhanced Mail (PEM)

(ii) Secure Electronic Transactions (SET)

(iii) Secure Sockets Layer (SSL)

Note: Each answer only requires one or two sentences.

Question 2: (5 marks)

Perform some research and discuss the following criteria on how biometric data in access control

systems are evaluated.

(i) False reject rate

(ii) False accept rate

(iii) Crossover error rate

Note: Each answer only requires one or two sentences.

Question 3: (5 marks)

Decipher the following ciphertext which was encrypted with the Caesar cipher.

TEBKFKQEBZLROPBLCERJXKBSBKQP

What is the most likely plaintext? Show your reasoning on how you arrive at the answer.

Question 4: (5 marks)

Decipher the following ciphertext which was encrypted with the Vigenere cipher.

TSMVM MPPCW CZUGX HPECP RFAUE IOBQW PPIMS FXIPC TSQPK SZNUL

OPACR DDPKT SLVFW ELTKR GHIZS FNIDF ARMUE NOSKR GDIPH WSGVL

EDMCM SMWKP IYOJS TLVFA HPBJI RAQIW HLDGA IYOUX

What is the key and the most likely plaintext? Show your reasoning on how you arrive at the

answer.

PART II: Big Data Analytics for Information Security (80%)

Task Summary

Big data analytics for security is a rising trend that is helping security analysts and tool vendors

do much more with data. Machine learning techniques can help security systems identify patterns

and threats with no prior definitions, rules or attack signatures, and with much higher accuracy.

However, to be effective, machine learning needs very big data. The challenge is storing so much

more data than ever before, analyzing it in a timely manner, and extracting new insights. An

organization that utilizes security and analytics tools can detect potential threats before they can

affect the company's assets and infrastructure. An important tool for organizations to manage

information security is through access control and only giving access to legitimate users. In this

section, we will focus on using biometrics for access control and information security.

Conduct a Big data science study in the security domain, for example, biometrics which utilizes

fingerprint, face, iris or other modalities. Other examples in the security domain will be fraud

analytics, intrusion detection, etc. Write an individual report on your Big data security and

analytics project. The report should be written in a clear and concise manner (and be no more

than 2000 words in length). You should start by exploring a biometric modality that interests you.

You need to identify a compact dataset (structured or unstructured) with a reasonable large size

and number of attributes/variables in your chosen modality or modalities which can be used for

the assessment. Your report should include the background of the chosen modality or modalities

and the data analytics problem you attempt to solve, aims and objectives, significance of your

study, and describe your analytics approach including the statistical method(s) and/or machine

learning technique(s) you used to address the problem. You are required to submit an individual

recorded video presentation to the Mediasite or other source which will be informed before the

submission date.

Context

In recent years, information security has taken center stage in the personal and professional lives

of the majority of the global population. Data breaches are a daily occurrence, and intelligent

adversaries target consumers, corporations, and governments with practically no fear of being

detected or facing consequences for their actions. This is all occurring while the systems, networks,

and applications that comprise the backbones of commerce and critical infrastructure are growing

ever more complex, interconnected, and unwieldy. Defenses built solely on the elements of faithbased security—unaided intuition and “best” practices—are no longer sufficient. The rising trend

is for organizations to adopt the proven tools and techniques being used in other disciplines to

take an evolutionary step into Data-Driven Security.

This assessment has been designed to help you build the necessary skills to achieve the following

learning objectives to fulfil the learning outcomes of this module. After completing this

assessment, you should be able to:

? Show proficiency with at least one data analytics software package; and

? Demonstrate awareness of issues related to computer and data security

By completing this assessment item, you will acquire the knowledge of information security, data

analytics and programming skills in Python to analyse the data from a security domain. You will

also acquire the presentation skills necessary to present the analysis of the results in your report

and recorded video to your audiences. This assessment will prepare you to address a Big data

security and analytics/science problem in the real world.

Task Instructions

(1) Write a short individual project proposal to describe your Big data security and analytics

project. Your project proposal should be written in a clear and concise manner (no more

than 500 words or 1-page A4 size). You start by exploring an area or domain in biometrics

which interests you. The project topic can be chosen from your target modality e.g.,

fingerprint, iris, face, palm print, etc. Show and discuss your proposal with the Teaching

Assistant (TA) during the laboratory sessions. Please note that no mark will be given for

this short proposal. However, this short proposal should serve as your first document to

plan for your Big data security and analytics project.

(2) Write a report on your Big data security and analytics project. The report should be written

in a clear and concise manner (and be no more than 2000 words in length). Your final

report should be detailed and address the following areas:

? Clearly define the problem definition in your Big data security and analytics project.

? Describe the significance of your Big data security and analytics project in the chosen

domain or area.

? Identify a compact dataset (structured or unstructured) with a reasonable large size

and number of attributes/variables in your chosen dataset. Some examples are shown

in the table below.

Note 1: On the one hand, students aiming for “Excellent” or “Very Good” grades will

pay attention to the complexity of the selected security dataset and advanced

approaches/steps to perform the analytics. For example, students could demonstrate

individual modality performances for palm print and knuckle print, and then show

that a combined multimodality (palm print and knuckle print) approach could give

higher performance. On the other hand, standard and/or conventional

approaches/steps for a single modality solution would be likely awarded an

“Adequate”, “Competent” or “Comprehensive” grade.

Security

Domain

Dataset

Fraud https://www.kaggle.com/datasets/kartik2112/fraud-detection

Palm print and

knuckle print

https://www.kaggle.com/datasets/michaelgoh/contactlessknuckle-palm-print-and-vein-dataset

Fingerprint https://www.kaggle.com/datasets/ruizgara/socofing

Hand tremor https://www.kaggle.com/datasets/hakmesyo/hand-tremordataset-for-biometric-recognition

Iris https://www.kaggle.com/datasets/naureenmohammad/mmuiris-dataset

? Highlight the project aim and objectives.

? Discuss the background of your chosen topic in the domain or area.

? Describe the analytics approach used.

? Describe how your analytics approach helped answer the problem and the statistical

method(s) and machine learning technique(s) you used.

? Describe all the steps you took to analyse your data.

? Discuss the results of the analysis.

? Include evidence, such as tables, graphs and plots from the programming

codes, to support your results.

(3) Prepare and record a short individual presentation (5 minutes) to introduce and explain

your Big data security and analytics project and its significance. Your presentation should

list the data science question or problem, describe your analytics approach and the

statistical and/or machine learning method(s) you used to address the data science

problem. Present and discuss the results of your analysis, and provide evidence

(screenshots) from your programming codes to support the results. Your presentation

should be clear, should be in no more than eight PowerPoint slides, and you should not

take more than 5 minutes to go through them. Your video presentation file cannot be

more than 50MB.

Note: Students MUST use the tools and software packages in the lab sessions to support their

data analytics involving practical scenarios.

Additionally, your final report should:

? be clearly structured (with well-organised content); and

? use the APA referencing style and include a reference list at the end.

For this assessment item, you are required to create programs using Python programming

language in software packages from your lab sessions to analyse your data. You are also required

to submit the programming source codes with the final report. Your programming source codes

should be:

? written in Python programming language;

? use the packages studied in lab e.g., pyspark for analysis, not external packages e.g.

pandas, numpy, seaborn and sklearn;

? can use purely visualization tool e.g., excel, Matplotlib to display, not analysis;

? well commented upon in relation to both the main program and each individual module,

such as the function module; and

? free of errors, such as syntax errors, runtime errors, etc.

Report Format

? Cover Page: This should include the Assessment Number, Assessment Title, Student

Name, Student ID and Student Email.

? Body of the report: This should include all the relevant section headings to address

each aspect as indicated/highlighted in the question and the marking rubric.

? References: Both your in-text and the references included in the ‘References’ section the

end of the report should adhere to the APA style.

? Glossary (Optional): This should include any terms frequently used in the report.

The following points are a general guide for the presentation of assessment items:

Assessments items should be typed;

? Use single spacing;

? Use a wide left margin (as markers need space to be able to include their comments);

? Use a standard 12-point font, such as Times New Roman, Calibri or Arial;

? Left-justify body text;

? Number your pages (excepting the cover page);

? Insert a header or footer that details your name and student number on each page;

? Always keep a copy (both hard and electronic) of your assessments; and

? Most importantly, always run a spelling and grammar check; however, remember, such

checks may not pick up all errors. You should still edit your work manually and carefully.

Referencing

It is essential that you use appropriate APA style for citing and referencing research.

Assessment Rubric (Part II – 50 marks)

Assessment

Attributes

Fail

0–39%

Adequate

40–49%

Competent

50–59%

Comprehensive

60–69%

Very Good

70–79%

Excellent

80–100%

State the Big data

security and

analytics/science

problem and dataset

guiding your study.

Briefly describe the

analytics approach

including the statistical

method(s) and/or

machine learning

technique(s) and how

your analytics approach

helped answer the

problem

Percentage for this

criterion = 5%

A statement of the

Big data science

problem and dataset

and a brief

description of

analytics approach

are not included.

The explanation of

analytics approach to

answer the Big data

science problem is

not adequate.

A statement of the

Big data science

problem and dataset

and a brief

description of

analytics approach

are included. How

the analytics

approach helped

answer the Big data

science problem is to

some extent

explained.

A statement of the

Big data science

problem and dataset

and a brief

description of

analytics approach

are included. How

the analytics

approach helped

answer the Big data

science problem is

competently

explained.

A statement of the Big

data science problem

and dataset and a brief

description of analytics

approach are included.

How the analytics

approach helped answer

the Big data science

problem is

comprehensively

explained.

A statement of the Big

data science problem

and dataset and a brief

description of analytics

approach are included.

How the analytics

approach helped

answer the Big data

science problem is

superbly explained but

with modest gaps.

A statement of the Big

data science problem

and dataset and a brief

description of analytics

approach are included.

How the analytics

approach helped

answer the Big data

science problem is

superbly explained.

Describe all the steps you

took to analyse your data.

See also Note 1.

Percentage for this

criterion = 5%

The steps are not

described in detail

and are not

systematic or logical

or consistent with

the selected data

analytics technique.

The steps are

described in some

detail but are not

systematic, logical or

consistent with the

selected data

analytics technique.

A genuine attempt

has been made to

describe the steps

and ensure that they

are somewhat

systematic, logical

and consistent with

the selected data

analytics technique.

The steps are described

in great detail and are

clearly systematic,

logical and consistent

with the selected data

analytics technique

The steps are

described in full detail

and are incredibly

systematic, logical and

consistent with the

selected data analytics

technique but with

modest gaps.

The steps are

described in full detail

and are incredibly

systematic, logical and

consistent with the

selected data analytics

technique.

Discuss the results of the

analysis. Provide

evidence, such as tables,

graphs and plots from

the programming codes,

The justification

makes no sense. No

evidence is provided

to support the results

in the report.

The justification

makes some sense.

Weak evidence is

provided to support

the results in the

report.

The justification

makes good sense.

Sufficient evidence is

provided to support

the results in the

report.

The justification makes

great sense. Strong

evidence is provided to

support the results in

the report.

The justification

makes perfect sense.

Comprehensive

evidence is provided to

support the results in

The justification

makes perfect sense.

Comprehensive

evidence is provided to

support the results in

the report.

to support your results.

See also Note 1.

Percentage for this

criterion = 20%

the report but with

modest gaps.

Programming source

codes (Python)

Percentage for this

criterion = 10%

Source codes are not

commented or are

only lightly

commented in the

main program and

for each module,

such as functions.

Codes contain errors.

Source codes are

partially commented

in the main program

and for each module,

such as functions.

Codes are completely

free from errors.

Source codes are

mostly commented

in the main program

and for each module,

such as functions.

Codes are completely

free of errors.

Source codes are well

commented in the main

program but are not

well commented for

each module, such as

functions. Codes are

completely free of

errors.

Source codes are very

well commented in the

main program and for

each module, such as

functions (with modest

gaps). Codes are

completely free of

errors.

Source codes are very

well commented in the

main program and for

each module, such as

functions. Codes are

completely free of

errors.

Content writing

Percentage for this

criterion = 5%

Rudimentary skills in

expression and

presentation of

ideas. Not all of the

material is relevant

and/or is presented

in a disorganised

manner.

The meaning is

apparent, but the

writing style is

not fluent or well

organised. Grammar

and spelling contain

many errors. Formal

English is not used.

Some skills in the

expression and

presentation of

ideas. The meaning is

apparent, but the

writing style is not

always fluent or well

organised.

Grammar and

spelling contain

several careless

errors. Formal

English is rarely used.

Sound skills in the

expression and clear

presentation of

ideas. The writing

style is mostly fluent

and appropriate to

the assessment

task/document type.

Grammar and

spelling contain a

few minor errors.

Formal English more

or less used.

Well-developed skills in

the expression and

presentation of ideas.

The writing style is

fluent and appropriate

to the assessment task/

document type.

Grammar and spelling

are accurate. Formal

English is mostly used.

Highly-developed skills

(with modest gaps) in

the expression and

presentation of ideas.

The writing style is

fluent and appropriate

to the assessment

task/document type.

Grammar and spelling

are accurate. Formal

English is used

throughout.

Highly-developed skills

in the expression and

presentation of ideas.

The writing style is

fluent and appropriate

to the assessment

task/document type.

Grammar and spelling

are accurate. Formal

English is used

throughout.

Uses the APA

referencing

style and provides

a reference list

Percentage for this

criterion = 5%

Substandard (or no)

referencing. Poor

quality (or no)

references.

Evidence of

rudimentary

referencing skills.

Good referencing in

both the reference

list and in-text

citations. Good

quality references.

Very good referencing in

both the reference list

and in-text citations.

High quality references.

Faultless referencing

(with modest gaps) in

both the reference list

and in-text citations.

High quality

references.

Faultless referencing in

both the reference list

and in-text citations.

High quality

references.

Assessment Rubric (Part II – 30 marks)

Assessment

Attributes

Fail

0–39%

Adequate

40–49%

Competent

50–59%

Comprehensive

60–69%

Very Good

70–79%

Excellent

80–100%

Your presentation

should be clear, should

be in no more than

eight slides, and you

should not take more

than 5 minutes to go

through them.

Percentage for this

criterion = 5%

The presentation is

not clear and did not

meet the number of

slides and time

constraints

The clarity of the

presentation is OK but

the presentation did

not meet the number

of slides and time

constraints

The clarity of the

presentation is

acceptable and the

number of slides and

time constraints are

sufficiently met

The clarity of the

presentation is good

and the number of

slides and time

constraints are closely

met

The presentation is

generally clear and

understandable and

precisely met the

number of slides and

time constraints

The presentation is

extremely clear and

completely

understandable and

precisely met the

number of slides and

time constraints

Your presentation

should list the Big data

security and

analytics/science

question or problem,

discuss the results of

your study, and provide

evidence that supports

the results.

Percentage for this

criterion = 15%

The results discussed

do not address the

Big data science

question or problem,

the results are not

described in any

detail and evidence

is not provided

Some of the results

discussed answer the

Big data science

question or problem,

the results are

somewhat described

but evidence is not very

convincing

A genuine attempt is

made at ensuring the

results discussed are

consistent with the Big

data science question

or problem, the results

are described in

acceptable detail and

some evidence are

presented

The results discussed

are sufficiently

consistent with the Big

data science or

problem, the results

are described in great

detail and supported

by solid evidence

The results discussed

are generally

consistent with the Big

data science question

or problem, the

results are superbly

described and

evidence is

exceptionally

convincing

The results discussed

are entirely consistent

with the Big data

science question or

problem, the results

are superbly described

and evidence is

exceptionally

convincing

Brief describe the

statistical and/or

machine learning

technique(s) you used

Percentage for this

criterion = 10%

The selected

statistical and/or

machine learning

technique(s) are not

adequately and are

not appropriate for

the Big data science

question or problem

The selected statistical

and/or machine learning

technique(s) are to

some extent described

but are hardly

appropriate for the Big

data science question

or problem

The selected statistical

and/or machine learning

technique(s) are

competently described

and is to some extent

appropriate for the Big

data science question

or problem

The selected statistical

and/or machine

learning technique(s)

are properly

described and is fairly

appropriate for the

Big data science

question or problem

The selected statistical

and/or machine

learning technique(s)

are for most parts

superbly described

and are fittingly

appropriate for the

Big data science

question or problem

The selected statistical

and/or machine

learning technique(s)

are superbly described

and are fittingly

appropriate for the

Big data science

question or problem