Group 5Abhishek Das, Bharat Jangir.

Project Overview

• We received a total responses of 119 responses.

• The division of the responses were as follows:

– 73 surveymonkey.com

– 26 Facebook and Reddit

– 20 Friends, Email and on paper responses.

• We first divided the task of collection from various sources in different parts:

– Bharat Jangir from Surveymonkey.com

– Abhishek Das from Facebook, Reddit

– Kevin Talmagde from Friends, Email and on paper

Surveymonkey.com

Benefits

• Easy to use

• Secured

• Trusted website

• We can format the results obtained in various different ways.

• Easy registration process and free

Survey Overview.

• We decided to keep the survey short to increase the number of responses otherwise people tend to lose interest. We wanted questions to be ubiquitous.

• We then trimmed our question set from 18 questions to 6 question. We did this by giving each question a score based on easiness and guaranteed level of response (1-5).

Questionnaire

• There were total of only 6 questions:

–Age

–Gender

–Highest level of education

–Do you use Antivirus? If so, which one?

–Reuse of username

–Reuse of password

Team OrganizationAbhishek Das 1) Implemented and decided the tools to be

used for analysis.2) Carried out the analysis.3) Performed and devised strategies to find association of attributes.4) Documented the report.

Bharat Jangir 1) Analysis of Data.2) Documentation.3) Algorithm and graph interpretation.

Kevin Talmagde 1) Analysis of Data.2) Documentation.3) Algorithm understanding and rule application.

Outcome

• We received a total responses of 119 in about 7 weeks time. Majority of the results came in from the initial weeks. Less number of females versus males who were open to taking surveys.

• Validity?

- Completely anonymous

- Website is secure

- Plenty of time to publicize about the survey

Analysis - By Gender

Analysis- By Education

Analysis - By Education

Analysis - By username reuse

Analysis - By password reuse

More about algorithms and implementation

● Password Reuse and use of anti virus: Initially by plotting a pie chart on the data set we found out that people do reuse passwords for more than one websites. We plotted a decision tree to support our claims.

● C4.5 / j48 algorithm was used to generate this decision tree.● This algorithm classifies the attributes on the basis of entropy.

• At each node it chooses an element that effectively splits the data on the basis of information gain.

• Higher the information gain, closer it lies to the node.

• Nodes are then split and re-split till the information gain is 0 or we reach end of splitting attributes.

• Education level affecting the use of antivirus: The table below shows how education level affects the user whether or not to use anti virus for protection.

More about algorithms and implementation• Apriori algorithm was used learn the association rule learning.

• It identifies the frequent datasets and extends them to larger item sets as long as those item appear frequently.

• This frequent item set is determined by Apriori rule to determine association rule.

• Example - Market analysis basket.

Tool used - WEKA

J48 classification tree algorithm in use to classify population for the use of anti virus vs education level.

Jitter plot showing the population classification

References:1) Blog report - Cyber security survey shows low internet security confidence across EU.

http://www.stemgroup.co.uk/news/cyber-security-survey-shows-low-internet-security-co nfidence-across-eu

2) Password reuse opens doors for cyber criminals

End-users must have a different password for every website and security domain – Feb-15,2011. http://www.infoworld.com/d/security-central/password-reuse-opens-doors-cyber-crimin als-457

3) University of Wakito, New Zealand, Weka documentation

http://grepcode.com/file/repo1.maven.org/maven2/nz.ac.waikato.cms.weka/weka-dev/3.7.9/w eka/classifiers/trees/J48.java#J48

4) University of Wakito, New Zealand, Weka documentation http://grepcode.com/file/repo1.maven.org/maven2/nz.ac.waikato.cms.weka/predictiveApriori/1 .0.2/weka/associations/PredictiveAprioriTest.java#PredictiveAprioriTest

Any Question???