代做DTS303TC、代写Python设计程序

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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
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and learning purposes, please type “yes” here.
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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