Spam Detection using N-gram Analysis and Machine Learning Techniques
| dc.contributor.author | Kaur, Simran | |
| dc.contributor.supervisor | Traore, Issa | |
| dc.date.accessioned | 2019-12-17T23:31:20Z | |
| dc.date.available | 2019-12-17T23:31:20Z | |
| dc.date.copyright | 2019 | en_US |
| dc.date.issued | 2019-12-17 | |
| dc.degree.department | Department of Electrical and Computer Engineering | |
| dc.degree.level | Master of Engineering M.Eng. | en_US |
| dc.description.abstract | There are many types of fraudulent activities happening today that open the loopholes of security, but email is a cheaper and widely known method for delivering false messages to potential victims. Spam is a form of email messages that is not only annoying for users but can provide a conduit for fraudulent or deceptive content delivery. In this project, a spam detector to identify an email as either spam or ham is built using n-gram analysis and supervised machine learning models. Three different algorithms are implemented and compared, namely naïve-Bayes, logistic regression and support vector machines (SVM). Experimental evaluation of the detector using a public dataset shows that the SVM and logistic regression attain the highest accuracy. | en_US |
| dc.description.scholarlevel | Graduate | en_US |
| dc.identifier.uri | http://hdl.handle.net/1828/11375 | |
| dc.language.iso | en | en_US |
| dc.rights | Available to the World Wide Web | en_US |
| dc.subject | N-grams | en_US |
| dc.subject | Spam detection | en_US |
| dc.title | Spam Detection using N-gram Analysis and Machine Learning Techniques | en_US |
| dc.type | project | en_US |