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. / Graduate
| Identifer | oai:union.ndltd.org:uvic.ca/oai:dspace.library.uvic.ca:1828/14293 |
| Date | 17 October 2022 |
| Creators | Siddeeq, Abubakar |
| Contributors | Gulliver, T. Aaron |
| Source Sets | University of Victoria |
| Language | English, English |
| Detected Language | English |
| Type | Thesis |
| Format | application/pdf |
| Rights | Available to the World Wide Web |
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