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Scalable Real Time Prediction Algorithm for Drive by Download Attack on Twitter Scalable Real Time Prediction Algorithm for Drive by Download Attack on Twitter

Scalable Real Time Prediction Algorithm for Drive by Download Attack on Twitter - PowerPoint Presentation

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Uploaded On 2018-03-16

Scalable Real Time Prediction Algorithm for Drive by Download Attack on Twitter - PPT Presentation

By Amir Javed Supervisor Dr Pete Burnap Prof Omer Rana Problem Identifies Trending Topics Trending topic lmao this tweet by user was nuts ShortURL User clicks on shortened URL ID: 653844

malicious url tweet algorithm url malicious algorithm tweet tweets benign machine malware 2016 model based log propagation user created

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Presentation Transcript

Slide1

Scalable Real Time Prediction Algorithm for Drive by Download Attack on Twitter

By

Amir Javed

Supervisor : Dr. Pete Burnap

Prof. Omer RanaSlide2

Problem

Identifies Trending Topics

#Trending topic

lmao this tweet by @user was nuts

Short_URL

User clicks on shortened URL

Gets re-directed to malicious page

Infects user system

virus Alert

Challenges

To quickly detect accounts that are spreading malware.

Drive by Download

The end algorithm should be able to handle

the large

tweet load.

To identify malicious websites before they disappear.Slide3

Predicting Algorithm- Training Phase

Extract URL from tweets

Use a honeypot (bait) to identify malicious URL

Algorithm learn what is malicious/ benign based on machine activity and tweet attributes

Malicious URL

Benign URLSlide4

Predicting Algorithm- Testing Phase

Extract URL from tweets

Algorithm segregates malicious/ benign URL based on initial machine activity and tweet attributes

Malicious URL

Benign URL

Training DataSlide5

Experimental ResultsWe captured tweets containing URL around two sporting events European

 Football 

Championship 2016

and Olympics 2016

First the tweets were segregated into malicious and benign using Capture HPC honeypot and based on these segregated URLs log files representing the time line of events were created to train four machine learning models.

Four machine learning models were used to train the model – J48 , Naïve Bays , Bayesnet and MLPThe highest F-measure was 99.2% for Bayesian Model The log file created for Olympics 2016 was tested using the model trained on Euro cup data.

The F-measure of 83.2% was achieved was for a log file created at 1 second.Slide6

Malware PropagationSlide7

Future PlanResearch to better understand the propagation of malicious tweet on Twitter so that malicious account can be removed at an early stage.

To incorporate prediction of most dominant node spreading the malware to stop malware propagation.

To deploy the Predictive algorithm on Cloud using the Comet-Cloud architecture to enable the algorithm to handle large number of tweets.Slide8

Thank you