Kanchana Ihalagedara Rajitha Kithuldeniya Supun weerasekara

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Presentation transcript:

Kanchana Ihalagedara Rajitha Kithuldeniya Supun weerasekara Supervised by Mr.Sampath Deegalla Feasibility of using Machine Learning to Access Control in Squid Proxy Server Kanchana Ihalagedara Rajitha Kithuldeniya Supun weerasekara 7/13/2019 Escape 2015

Internet in Educational Institutes Mainly for educational purposes. What happens if users priority is not the intended purpose. Network congestions Wastage of resources Affects individual user performance negatively 7/13/2019 Escape 2015

Blocking Web Sites in Proxy Server Squid ACLs - Text file of blacklists SquidGuard - External databases DansGuardian - Content filter 7/13/2019 Escape 2015

World Wide Web is Growing 672,985,183 - 2013 968,882,453 - 2014 295,897,270 From www.internetlivestats.com Manually blacklisting web sites is impossible Related products are not updated with the growing web 7/13/2019 Escape 2015

Dynamic automated method Automated web classification is required Machine Learning is used in automated web classification 7/13/2019 Escape 2015

Over View of Our Solution Copy client request Check URL Get web content Classify web content Update the blacklist 7/13/2019 Escape 2015

Machine Learning in Web Classification Several web classification researches can be found Frequently used algorithms Naïve Byes Support vector machine Nearest neighbor Classification requires a data set Set of URLs labeled as educational or non educational 7/13/2019 Escape 2015

Data Collection & Preprocessing Preprocess Squid server log Preprocess DMOZ data set Create labeled URLs Get web content Create training data set 7/13/2019 Escape 2015

Model Creation & Testing Four models were created from WEKA(small data set) Data set with two hundred records 10 – fold cross validation for testing Algorithm Accuracy(%) PRISM 74.5 C4.5 (J48 in WEKA) 83.0 Naïve bayes 95.0 Support Vector Machines 95.5 7/13/2019 Escape 2015

Model Creation & Testing Three models using Python (larger dataset) Data set of 4000 records Separate data set of 1000 records for Testing Algorithm Accuracy Naïve Bayes multinomial 92.9% SVC 77.5% Linear SVC 98.9% 7/13/2019 Escape 2015

Feature Selection in Linear SVC 7/13/2019 Escape 2015

Principal Component Analysis 7/13/2019 Escape 2015

Future Work Consider more content (Meta data) Other Languages (Sinhala) Image processing can be added 7/13/2019 Escape 2015

Thank You! 7/13/2019 Escape 2015