Malicious URL Detection using Machine Learning
| dc.contributor.author | Siddeeq, Abubakar | |
| dc.contributor.supervisor | Gulliver, T. Aaron | |
| dc.date.accessioned | 2022-10-17T16:54:03Z | |
| dc.date.available | 2022-10-17T16:54:03Z | |
| dc.date.copyright | 2022 | en_US |
| dc.date.issued | 2022-10-17 | |
| dc.degree.department | Department of Electrical and Computer Engineering | en_US |
| dc.degree.level | Master of Applied Science M.A.Sc. | en_US |
| dc.description.abstract | Malicious URL detection is important for cyber security experts and security agencies. With the drastic increase in internet usage, the distribution of such malware is a serious issue. Due to the wide variety of this malware, detection even with antivirus software is difficult. More than 12.8 million malicious URL websites are currently running. In this thesis, several machine learning classifiers along with ensemble methods are used to formulate a framework to detect this malware. Principal component analysis, k-fold cross-validation, and hyperparameter tuning are used to improve performance. A dataset from Kaggle is used for classification. Accuracy, precision, recall, and f-score are used as metrics to determine the model performance. Moreover, model behavior with a majority of one label in the dataset is also examined as is typical in the real world. | en_US |
| dc.description.scholarlevel | Graduate | en_US |
| dc.identifier.uri | http://hdl.handle.net/1828/14293 | |
| dc.language | English | eng |
| dc.language.iso | en | en_US |
| dc.rights | Available to the World Wide Web | en_US |
| dc.subject | Machine learning | en_US |
| dc.subject | URL | en_US |
| dc.subject | Malicious URL | en_US |
| dc.subject | ML | en_US |
| dc.title | Malicious URL Detection using Machine Learning | en_US |
| dc.type | Thesis | en_US |