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Python辅导 | CSE 231 Fall 2019 Project #9

使用Python分析网络安全漏洞相关的数据

CSE 231 Fall 2019
Project #9
This assignment focuses on the design, implementation and testing of a Python program which
uses control structures to solve the problem described below.
It is worth 55 points (5.5% of course grade) and must be completed no later than 11:59 PM on
Monday, November 18, 2019.
Assignment Overview
(learning objectives)
This assignment will give you more experience on the use of:
• lists
• dictionaries
• data structures
• functions
• iteration
• data analysis
The goal of this project is to analyze data relevant to the biggest cybersecurity breaches in the
past.
Assignment Background
Cybersecurity breaches are unfortunately becoming more and more common. These can
sometimes be attributed to clever, sophisticated attacks and other times to lax security. However,
the result is the same – records are lost such that the confidentiality, integrity, and availability of
sensitive information is affected.
Project Description
This project focuses on analyzing a publicly available dataset containing information about some
infamous breaches that have occurred in the past.
open_file(message) -> fp
This function taken in a prompt message (string) and returns a file pointer. You likely have a
copy from a previous project. It repeatedly prompts for a file until one is successfully opened. It
should have a try-except statement. By default (when the user does not provide a filename), this
function opens the file ‘breachdata.csv’. Important: remember to open the file with the
correct encoding as shown below.
fp = open(filename, encoding=’utf8′)
updated 11/7/2019
build_dict(reader) -> dictionary
This function accepts a CSV reader as input and returns the required dictionary. It iterates over
the CSV reader and with each iteration, extracts the needed data and remove any extra
whitespaces. You need to skip the header line before start reading the data:
next(reader,None)
The data to be extracted is:
entity – index 0 (string)
records lost – index 2 (int)
year – index 3 (int)
story – index 4 (string)
sector – index 5 (string)
method – index 6 (string)
news sources – index 11 (list of strings)
Valid data checks:
• If there are multiple news sources at index 11, they will be separated by a comma (‘,’).
These should all be stored in a list. If the news sources field is empty ignore that line.
• Treat all missing numeric values of “records lost” as a 0. You need to remove all
occurrences of ‘,’ in the records lost before converting to integer. Hint: use
.replace()
• If any of these pieces of data is missing (other than “records lost”), e.g. the field is empty
or spaces, ignore that line of data.
• If the year is invalid (i.e. not an int), ignore that line.
The overall data structure is a master dictionary. This master dictionary has key ‘entity’ (string),
and value as a list. This list contains tuples for each instance of this entity observed in the data
file. For example, AOL occurs 3 times in the breachdata.csv CSV file, so there are three
tuples within the list for AOL. These 3 tuples are color coded below.
{‘AOL’:[
({‘AOL’: (92000000, 2004, ‘Jun 2004. A former America Online software
engineer stole 92 million screen names and e-mail addresses and sold them to
spammers who sent out up to 7 billion unsolicited e-mails.’, [‘CNN’])},
{2004: (‘web’, ‘inside job’)}),
({‘AOL’: (20000000, 2006, ‘Aug 2006. Durp. AOL VOLUNTARILY released search
data for roughly 20 million web queries from 658,000 anonymized users of the
service. No one is quite sure why.’, [‘Tech Crunch’])}, {2006: (‘web’,
‘oops!’)}),
({‘AOL’: (2400000, 2014, “Apr 2014. Users’ accounts were compromised to send
out spam messages.”, [‘NBC News’])}, {2014: (‘web’, ‘hacked’)})
]}
Within each tuple are two dictionaries. The first dictionary has the key ‘entity’ (string) and its
value is a tuple that contains (records lost(int), year(int), story(string), news_source(list of
strings)). The second dictionary within this tuple has its key as year(int) and value as a tuple that
contains (sector(string), method(string)). Let’s assume that D1 and D2 are the two dictionaries,
then the values of the master dictionary are [(D1,D2)]
top_rec_lost_by_entity(dictionary) -> list
This function accepts the breach dictionary as created by the build_dict function above and
returns a sorted list (in descending order of records lost) of the top 10 entities that lost the most
records. To break ties, the returned list should be sorted by entity alphabetically. The returned list
will contain 10 tuples, each tuple containing the entity name and total records lost by that entity.
For example, in the case of AOL, the tuple will be as follows:
(‘AOL’, 114400000)
From the AOL entry shown for build_dict you can calculate that AOL lost a total of
(92000000+20000000+2400000) 114400000 records.
Hint: use itemgetter from the operator module and the key argument to the sort routines
on index 1 of the tuple.
records_lost_by_year(dictionary) -> list
This function accepts the breach dictionary and produces a sorted list of total records lost within
each year. This is similar to the previous function, except that instead of counting records lost by
entities, we are counting records lost by year. Also, instead of returning only the top 10, this
function should return all records by year. The list will contain tuples such that each tuple
contains the year and the corresponding total records lost in that year. For example:
(2018, 4062466578)
The list should be sorted in descending order by total records lost and by year (to break ties)
Hint: you could start by creating a dictionary where ‘year’ is the key and the record lost for
that year are the values.
top_methods_by_sector(dictionary) -> dictionary
This function takes in the breach dictionary and returns a dictionary of dictionaries. The returned
dictionary contains keys for all sectors found in the breach dictionary (e.g. ‘web’, ‘tech’ etc.).
The values of this dictionary are dictionaries that contain the attack vector (method) as key and
the corresponding count as value. For example,
{‘web’:{‘inside job’: 1, ‘oops!’: 2, ‘hacked’: 81, ‘unknown’: 1,
‘poor security’: 12}}
Within the ‘web’ sector, we have counted 81 instances of the method ‘hacked’, 12 instances of
‘poor security’ etc. The returned dictionary should be sorted in ascending order by the sector
name.
Hint: Remember that dictionaries are insertion ordered. Insertion Ordered means the order in
which we are inserting the data in the dictionary is maintained.
top_rec_lost_plot(names,records)
This function is provided. It plots a bar graph for both top records lost by entities and the total
records lost by year when the relevant datasets are provided correctly. Call this with the
appropriate data within main().
top_methods_by_sector_plot(methods_list)
This function is provided. It plots a pie chart for distribution of methods of attack used within
each sector. Call this within main() with the appropriate input.
main()
This function prints provided BANNER and MENU and then asks the user to make a choice
between the various available options shown in the menu. If the choice is 1,2,3, or 4, and it is the
first time asking for a choice, it calls the open_file() function with the appropriate message
string to obtain a handle to the file pointer. Next, csv.reader is used to read data from the file
pointer. This reader is then sent to build_dict() function to build the breach dictionary.
1. Option 1: If the choice is 1, the program calculates the top 10 entities that lost the most
records by calling the function top_rec_lost_by_entity() and displays the
results to the user in descending order Each displayed row contains the rank, the entity
and the number of records lost. Each row is separated by dashed line containing 45
dashes (“-“*45). Use the following string formatting for each row:
“[ {:2d} ] | {:15.10s} | {:10d}”
It then asks the user if they want to plot the results. Depending on user’s response, plot
can be displayed. The corresponding plot function, top_rec_lost_plot(), expects
two lists as function input, the first list is the names of all entities in descending order of
most records lost, the second list contains the corresponding quantities. For example:
names = [‘Aadhaar’, ‘Yahoo’, ‘River City Media’, …]
records = [2100000000, 1532000000, 1370000000, …]
2. Option 2: If the choice is 2, the program calculates the records lost in each year by calling
the function records_lost_by_year() and displays the results to the user in
descending order. Each displayed row contains the rank, the year (string) and the number
of records lost. Each row is separated by dashed line containing 45 dashes (“-“*45).
Use the following string formatting for each row:
“[ {:2d} ] | {:15.10s} | {:10d}”
It then asks the user if they want to plot the results. Depending on user’s response, plot
can be displayed. The corresponding plot function, top_rec_lost_plot(), expects
two lists as function input, the first list is the years in descending order of most records
lost, the second list contains the corresponding quantities. For example:
years = [2018, 2017, 2019, …]
records = [4062466578, 2455191309, 2016515298, …]
3. If the choice is 3, the program calculates the top methods used for breaching each sector
by calling the function top_methods_by_sector() and displays the names of
sectors discovered separated by a space. It then asks the user to input a sector name. The
program should keep asking for a valid sector name. Once valid, the methods and their
counts pertaining to this sector are then displayed to the user in descending order by
count. Each displayed row contains the rank, the method and its counts. Each row is
separated by dashed line containing 45 dashes (“-“*45). Use the following string
formatting for each row:
“[ {:2d} ] | {:15.10s} | {:10d}”
Finally, the program asks if the user wants to plot the results. Results are plotted by the
top_methods_by_sector_plot()function based on the user’s response.
4. If the choice is 4, the user is asked to input the name of an entity (e.g. AOL). The user is
then shown all stories discovered within the breach dictionary pertaining to that entity. If
an incorrect entity name is entered (one that is not found in the dictionary) then an error
message is displayed and user is asked to enter the entity name again.
Note that the overall data structure is a master dictionary. This master dictionary has key
‘entity’ (string), and value as a list. This list contains tuples for each instance of this
entity observed in the data file including stories. For example, AOL occurs 3 times in the
breachdata.csv CSV file. {‘AOL’:[
({‘AOL’: (92000000, 2004, ‘Jun 2004. A former America Online software
engineer stole 92 million screen names and e-mail addresses and sold
them to spammers who sent out up to 7 billion unsolicited e-mails.’,
[‘CNN’])}, {2004: (‘web’, ‘inside job’)}),
({‘AOL’: (20000000, 2006, ‘Aug 2006. Durp. AOL VOLUNTARILY released
search data for roughly 20 million web queries from 658,000 anonymized
users of the service. No one is quite sure why.’, [‘Tech Crunch’])},
{2006: (‘web’, ‘oops!’)}),
({‘AOL’: (2400000, 2014, “Apr 2014. Users’ accounts were compromised to
send out spam messages.”, [‘NBC News’])}, {2014: (‘web’, ‘hacked’)})
]}
Your program should output 3 stories:
[ + ] Found 3 stories:
[ + ] Story 1: Jun 2004. A former America Online software engineer
stole 92 million screen names and e-mail addresses and sold them to
spammers who sent out up to 7 billion unsolicited e-mails.
[ + ] Story 2: Aug 2006. Durp. AOL VOLUNTARILY released search data for
roughly 20 million web queries from 658,000 anonymized users of the
service. No one is quite sure why.
[ + ] Story 3: Apr 2014. Users’ accounts were compromised to send out
spam messages.
5. If the choice is 5, the program prints theclosing message and exits.
Note: the program needs to catch exceptions in all the input sequences. For example, if the
choice is not 1,2,3,4 or 5, then an error message is displayed, and user is asked for input again.
Similarly, for sector name in choice 3, if the user enters an invalid sector name then an error
message is displayed, and the user is asked to input the sector name again. Also, for ensity name
in choice 4, if the user enters an invalid entity name then an error message is displayed, and the
user is asked to input the entity name again.
Assignment Deliverables
The deliverable for this assignment is the following file:
proj09.py – the source code for your Python program
Be sure to use the specified file name and to submit it for grading via the Mimir before the
project deadline.
Assignment Notes
1. Use itemgetter() from the operator module to specify the key for sorting.
2. Items 1-9 of the Coding Standard will be enforced for this project.
Suggested Procedure
• Solve the problem using pencil and paper first. You cannot write a program until you
have figured out how to solve the problem. This first step is best done collaboratively
with another student. However, once the discussion turns to Python specifics and the
subsequent writing of Python statements, you must work on your own.
• Write a simple version of the program. Run the program and track down any errors.
• Use the debugger available in Spyder to locate and resolve errors. Set breakpoints right
before the instructions where you perceive the program begins and then step through the
code one instruction at a time. While doing this, keep an eye on the ‘variable explorer’
window in Spyder to observe the change in variables.
• Use the Mimir system to turn in the first version of your program.
• Cycle through the steps to incrementally develop your program:
o Edit your program to add new capabilities.
o Run the program and fix any errors.
• Use the Mimir system to submit your final version.
• Be sure to log out when you leave the room, if you’re working in a public lab.
Test 1
[ 1 ] Most records lost by entities
[ 2 ] Records lost by year
[ 3 ] Top methods per sector
[ 4 ] Search stories
[ 5 ] Exit
[ ? ] Choice: 1
[ ? ] Enter the file name:
[ + ] Most records lost by entities…
———————————————
[ 1 ] | Aadhaar | 2100000000
———————————————
[ 2 ] | Yahoo | 1532000000
———————————————
[ 3 ] | River City | 1370000000
———————————————
[ 4 ] | First Amer | 885000000
———————————————
[ 5 ] | Spambot | 711000000
———————————————
[ 6 ] | Friend Fin | 412000000
———————————————
[ 7 ] | Marriott H | 383000000
———————————————
[ 8 ] | Twitter | 330250000
———————————————
[ 9 ] | MongoDB | 275265298
———————————————
[ 10 ] | Chinese re | 202000000
[ ? ] Plot (y/n)? n

[ 1 ] Most records lost by entities
[ 2 ] Records lost by year
[ 3 ] Top methods per sector
[ 4 ] Search stories
[ 5 ] Exit
[ ? ] Choice: 5
[ + ] Done. Exiting now…
Test 2
[ 1 ] Most records lost by entities
[ 2 ] Records lost by year
[ 3 ] Top methods per sector
[ 4 ] Search stories
[ 5 ] Exit
[ ? ] Choice: 2
[ ? ] Enter the file name:
[ + ] Most records lost in a year…
———————————————
[ 1 ] | 2018 | 4062466578
———————————————
[ 2 ] | 2017 | 2455191309
———————————————
[ 3 ] | 2019 | 2016515298
———————————————
[ 4 ] | 2016 | 1801353869
———————————————
[ 5 ] | 2013 | 1653263579
———————————————
[ 6 ] | 2015 | 474847000
———————————————
[ 7 ] | 2014 | 328674396
———————————————
[ 8 ] | 2009 | 254152778
———————————————
[ 9 ] | 2012 | 232898177
———————————————
[ 10 ] | 2011 | 199841734
———————————————
[ 11 ] | 2007 | 150597405
———————————————
[ 12 ] | 2004 | 92000000
———————————————
[ 13 ] | 2008 | 88455500
———————————————
[ 14 ] | 2006 | 46825000
———————————————
[ 15 ] | 2005 | 44100000
———————————————
[ 16 ] | 2010 | 10149285 [ ? ] Plot (y/n)? n

[ 1 ] Most records lost by entities
[ 2 ] Records lost by year
[ 3 ] Top methods per sector
[ 4 ] Search stories
[ 5 ] Exit
[ ? ] Choice: 5
[ + ] Done. Exiting now…
Test 3
[ 1 ] Most records lost by entities
[ 2 ] Records lost by year
[ 3 ] Top methods per sector
[ 4 ] Search stories
[ 5 ] Exit
[ ? ] Choice: 6
[ – ] Incorrect input. Try again.

[ 1 ] Most records lost by entities
[ 2 ] Records lost by year
[ 3 ] Top methods per sector
[ 4 ] Search stories
[ 5 ] Exit
[ ? ] Choice: youdoyou
[ – ] Incorrect input. Try again.

[ 1 ] Most records lost by entities
[ 2 ] Records lost by year
[ 3 ] Top methods per sector
[ 4 ] Search stories
[ 5 ] Exit
[ ? ] Choice: -435gh
[ – ] Incorrect input. Try again.

[ 1 ] Most records lost by entities
[ 2 ] Records lost by year
[ 3 ] Top methods per sector
[ 4 ] Search stories
[ 5 ] Exit
[ ? ] Choice: 5
[ + ] Done. Exiting now…
Test 4
[ 1 ] Most records lost by entities
[ 2 ] Records lost by year
[ 3 ] Top methods per sector
[ 4 ] Search stories
[ 5 ] Exit
[ ? ] Choice: 4
[ ? ] Enter the file name:
[ ? ] Name of the entity (case sensitive)? theBeatles
[ – ] Entity not found. Try again.
[ ? ] Name of the entity (case sensitive)? Liverpool
[ – ] Entity not found. Try again.
[ ? ] Name of the entity (case sensitive)? AOL
[ + ] Found 3 stories:
[ + ] Story 1: Jun 2004. A former America Online software engineer
stole 92 million screen names and e-mail addresses and sold them to
spammers who sent out up to 7 billion unsolicited e-mails.
[ + ] Story 2: Aug 2006. Durp. AOL VOLUNTARILY released search data
for roughly 20 million web queries from 658,000 anonymized users of
the service. No one is quite sure why.
[ + ] Story 3: Apr 2014. Users’ accounts were compromised to send out
spam messages.

[ 1 ] Most records lost by entities
[ 2 ] Records lost by year
[ 3 ] Top methods per sector
[ 4 ] Search stories
[ 5 ] Exit
[ ? ] Choice: 5
[ + ] Done. Exiting now…
Test 5

[ 1 ] Most records lost by entities
[ 2 ] Records lost by year
[ 3 ] Top methods per sector
[ 4 ] Search stories
[ 5 ] Exit
[ ? ] Choice: 1
[ ? ] Enter the file name: breachdata_small.csv
[ + ] Most records lost by entities…
———————————————
[ 1 ] | AOL | 112000000
———————————————
[ 2 ] | Cardsystem | 40000000
———————————————
[ 3 ] | Citigroup | 3900000
———————————————
[ 4 ] | Ameritrade | 200000
[ ? ] Plot (y/n)? n

[ 1 ] Most records lost by entities
[ 2 ] Records lost by year
[ 3 ] Top methods per sector
[ 4 ] Search stories
[ 5 ] Exit
[ ? ] Choice: 2
[ + ] Most records lost in a year…
———————————————
[ 1 ] | 2004 | 92000000
———————————————
[ 2 ] | 2005 | 44100000
———————————————
[ 3 ] | 2006 | 20000000
[ ? ] Plot (y/n)? n

[ 1 ] Most records lost by entities
[ 2 ] Records lost by year
[ 3 ] Top methods per sector
[ 4 ] Search stories
[ 5 ] Exit
[ ? ] Choice: 3
[ + ] Loaded sector data.
financial web
[ ? ] Sector (case sensitive)? web
[ + ] Top methods in sector web
———————————————
[ 1 ] | inside job | 1
———————————————
[ 2 ] | oops! | 1
[ ? ] Plot (y/n)? n

[ 1 ] Most records lost by entities
[ 2 ] Records lost by year
[ 3 ] Top methods per sector
[ 4 ] Search stories
[ 5 ] Exit
[ ? ] Choice: 4
[ ? ] Name of the entity (case sensitive)? Citigroup
[ + ] Found 1 stories:
[ + ] Story 1: Jun 2005. Blame the messenger! A box of computer
tapes containing information on 3.9 million customers was lost
by United Parcel Service (UPS) while in transit to a credit
reporting agency.

[ 1 ] Most records lost by entities
[ 2 ] Records lost by year
[ 3 ] Top methods per sector
[ 4 ] Search stories
[ 5 ] Exit
[ ? ] Choice: 4
[ ? ] Name of the entity (case sensitive)? citigroup
[ – ] Entity not found. Try again.
[ ? ] Name of the entity (case sensitive)? AOL
[ + ] Found 2 stories:
[ + ] Story 1: Jun 2004. A former America Online software
engineer stole 92 million screen names and e-mail addresses and
sold them to spammers who sent out up to 7 billion unsolicited
e-mails.
[ + ] Story 2: Aug 2006. Durp. AOL VOLUNTARILY released search
data for roughly 20 million web queries from 658,000 anonymized
users of the service. No one is quite sure why.

[ 1 ] Most records lost by entities
[ 2 ] Records lost by year
[ 3 ] Top methods per sector
[ 4 ] Search stories
[ 5 ] Exit
[ ? ] Choice: 5
[ + ] Done. Exiting now…
Test 6 (plotting; no Mimir test)
[ 1 ] Most records lost by entities
[ 2 ] Records lost by year
[ 3 ] Top methods per sector
[ 4 ] Search stories
[ 5 ] Exit
[ ? ] Choice: 1
[ ? ] Enter the file name:
[ + ] Most records lost by entities…
———————————————
[ 1 ] | Aadhaar | 2100000000
———————————————
[ 2 ] | Yahoo | 1532000000
———————————————
[ 3 ] | River City | 1370000000
———————————————
[ 4 ] | First Amer | 885000000
———————————————
[ 5 ] | Spambot | 711000000
———————————————
[ 6 ] | Friend Fin | 412000000
———————————————
[ 7 ] | Marriott H | 383000000
———————————————
[ 8 ] | Twitter | 330250000
———————————————
[ 9 ] | MongoDB | 275265298
———————————————
[ 10 ] | Chinese re | 202000000
[ ? ] Plot (y/n)? y

[ 1 ] Most records lost by entities
[ 2 ] Records lost by year
[ 3 ] Top methods per sector
[ 4 ] Search stories
[ 5 ] Exit
[ ? ] Choice: 2
[ + ] Most records lost in a year…
———————————————
[ 1 ] | 2018 | 4062466578
———————————————
[ 2 ] | 2017 | 2455191309
———————————————
[ 3 ] | 2019 | 2016515298
———————————————
[ 4 ] | 2016 | 1801353869
———————————————
[ 5 ] | 2013 | 1653263579
———————————————
[ 6 ] | 2015 | 474847000
———————————————
[ 7 ] | 2014 | 328674396
———————————————
[ 8 ] | 2009 | 254152778
———————————————
[ 9 ] | 2012 | 232898177
———————————————
[ 10 ] | 2011 | 199841734
———————————————
[ 11 ] | 2007 | 150597405
———————————————
[ 12 ] | 2004 | 92000000
———————————————
[ 13 ] | 2008 | 88455500
———————————————
[ 14 ] | 2006 | 46825000
———————————————
[ 15 ] | 2005 | 44100000
———————————————
[ 16 ] | 2010 | 10149285 [ ? ] Plot (y/n)? y

[ 1 ] Most records lost by entities
[ 2 ] Records lost by year
[ 3 ] Top methods per sector
[ 4 ] Search stories
[ 5 ] Exit
[ ? ] Choice: 3
[ + ] Loaded sector data.
academic app energy financial gaming government healthcare legal
media military retail tech telecoms transport web
[ ? ] Sector (case sensitive)? academic
[ + ] Top methods in sector academic
———————————————
[ 1 ] | hacked | 6
———————————————
[ 2 ] | lost devic | 3
———————————————
[ 3 ] | oops! | 1
———————————————
[ 4 ] | poor secur | 1
[ ? ] Plot (y/n)? y

[ 1 ] Most records lost by entities
[ 2 ] Records lost by year
[ 3 ] Top methods per sector
[ 4 ] Search stories
[ 5 ] Exit
[ ? ] Choice: 5
[ + ] Done. Exiting now…
Function Tests: for each there is an input and an “instructor value” that the instructor’s
version of the function returned. The values shown below are for the small dataset:
breachdata_small.csv.
build_dict(reader)
breach_dict = build_dict(reader)
{‘AOL’:
[({‘AOL’: (92000000, 2004, ‘Jun 2004. A former America Online
software engineer stole 92 million screen names and e-mail
addresses and sold them to spammers who sent out up to 7 billion
unsolicited e-mails.’, [‘CNN’])}, {2004: (‘web’, ‘inside
job’)}),
({‘AOL’: (20000000, 2006, ‘Aug 2006. Durp. AOL VOLUNTARILY
released search data for roughly 20 million web queries from
658,000 anonymized users of the service. No one is quite sure
why.’, [‘Tech Crunch’])}, {2006: (‘web’, ‘oops!’)})],
‘Ameritrade Inc.’:
[({‘Ameritrade Inc.’: (200000, 2005, ‘Apr 2005. Computer backup
tape containing personal information was lost.’, [‘NBC’])},
{2005: (‘financial’, ‘lost device’)})],
‘Citigroup’:
[({‘Citigroup’: (3900000, 2005, ‘Jun 2005. Blame the messenger!
A box of computer tapes containing information on 3.9 million
customers was lost by United Parcel Service (UPS) while in
transit to a credit reporting agency.’, [‘NY Times’])}, {2005:
(‘financial’, ‘lost device’)})],
‘Cardsystems Solutions Inc.’:
[({‘Cardsystems Solutions Inc.’: (40000000, 2005, “Jun 2005.
CardSystems was fingered by MasterCard after it spotted fraud on
credit card accounts and found a common thread, tracing it back
to CardSystems. An unauthorized entity put a specific code into
CardSystems’ network, enabling the person or group to gain
access to the data. It’s not clear how many of the 40 million
accounts were actually stolen.”, [‘Wired’])}, {2005:
(‘financial’, ‘hacked’)})]}
top_rec_lost_by_entity(breach_dict)
[(‘AOL’, 112000000),
(‘Cardsystems Solutions Inc.’, 40000000),
(‘Citigroup’, 3900000),
(‘Ameritrade Inc.’, 200000)]
records_lost_by_year(breach_dict)
[(2004, 92000000), (2005, 44100000), (2006, 20000000)]
top_methods_by_sector(breach_dict)
{‘financial’: {‘lost device’: 2, ‘hacked’: 1},
‘web’: {‘inside job’: 1, ‘oops!’: 1}}
Grading Rubric
Computer Project #09 Scoring Summary
General Requirements:
4 pts
Coding Standard 1-9
(descriptive comments, function headers, etc…)
Function Tests:
2 pts open_file (no Mimir test)
5 pts build_dict()
5 pts top_methods_by_sector()
5 pts top_rec_lost_by_entity()
5 pts records_lost_by_year()
Program Tests
5 pts Test1
5 pts Test2
4 pts Test3
5 pts Test4
5 pts Test5
5 pts Test6 (plotting; manual, no Mimir test)
-3 for plotting (-1 for every plot)
-2 for the output


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